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Job Description Open to Peoria, IL and Irving, TX as well Position's Contributions to Work Group : - The Lead Software Engineer in the Emerging Technology group is a hands-on technical leader responsible for designing, building, piloting, and scaling next-generation software solutions that apply emerging technologies to high-value business problems. - This role combines deep software engineering expertise with practical innovation, helping translate exploratory concepts into secure, reliable, maintainable, and production-ready capabilities Typical task breakdown: - Lead the architecture, design, development and testing of modern software solutions, prototypes, pilots, and reusable platform capabilities. - Evaluate, prototype, and apply emerging technologies such as generative AI, agentic workflows, retrieval-augmented generation, cloud-native services, APIs, automation, and data-driven application patterns. - Translate business opportunities, product goals, and technical requirements into scalable software designs, solution patterns, technical roadmaps, and implementation plans. - Mentor engineers, provide technical direction, facilitate design reviews, and promote a culture of experimentation, learning, engineering excellence, and responsible technology adoption. - Collaborate with product managers, architects, data scientists, cybersecurity, infrastructure, UX, and business stakeholders to deliver solutions aligned to organizational goals and risk expectations. - Define and implement technical evaluation methods, including proof-of-concept success criteria, model or system performance measures, quality gates, automated testing, monitoring, and production readiness reviews. - Identify technical risks, dependencies, integration needs, security considerations, and operational constraints early in the solution lifecycle and recommend practical mitigation strategies. Interaction with team : - Team consists of roughly 10 folks in US and 7 folks offshore. - in the Chicago area, rest in the US - Large team, there are 2 PODs leads (technical lead/mentor others) , are responsible for a piece of portfolio at any given time. Helping to shape work items at a higher level. They also get to work on the challenging parts as well. Work environment : 2-3 days a week in office and for the other offices, it is as needed basis. A full return could happen at any point. Education & Experience Required: - Bachelor's degree (technology related field) with 8+ years exp in this capacity - No degree then 10_ years in this capacity. Required Technical Skills (Required) Decision Making and Critical Thinking: Knowledge of the decision-making - process and associated tools and techniques; ability to accurately analyze situations and reach productive decisions based on informed judgment. - Software Development: Knowledge of software development tools and activities; ability to produce software products or systems in line with product requirements. - Software Development Life Cycle: Knowledge of software development life cycle; ability to use a structured methodology for delivering and managing new or enhanced software products to the marketplace. - Software Integration Engineering: Knowledge of software integration processes and functions; ability to design, develop and maintain interfaces and linkage to alternative platforms and software packages. - Software Product Design/Architecture: Knowledge of software product design; ability to convert market requirements into the software product design. - Software Product Technical Knowledge: Knowledge of technical aspects of a software products; ability to design, configure and integrate technical aspects of software products - Software Product Testing: Knowledge of software product testing; ability to design, plan, and execute testing strategies and tactics to ensure software product quality and adherence to stated requirements. Considerations For Top Candidates: • Bachelor's degree in Computer Science, Software Engineering, Computer Engineering, Information Technology, or a related technical discipline; equivalent practical experience may be considered. • Progressive professional experience in software engineering, including hands-on development of complex applications, distributed systems, APIs, platforms, or cloud-native services. • Demonstrated ability to lead technical work across the full software development lifecycle, including requirements analysis, architecture, design, coding, testing, deployment. • Strong proficiency in one or more modern programming languages such as , or similar languages. • Experience designing scalable, secure, observable, and maintainable software using modern architecture patterns, APIs, automated testing, CI/CD, and DevOps practices. • Working knowledge of cloud platforms, containerization, infrastructure automation, integration patterns, and production support practices. • Ability to communicate complex technical topics clearly to engineering, product, architecture, security, and business stakeholders. • Demonstrated analytical, problem-solving, and decision-making skills in ambiguous or rapidly evolving technical environments. Preferred Qualifications • Experience building or integrating AI, machine learning, generative AI, large language model, agentic, retrieval-augmented generation, vector database, or prompt-based solutions. • Experience taking emerging technology concepts from proof of concept through pilot, production hardening, governance review, adoption, and operational support. • Familiarity with responsible AI practices, model evaluation, guardrails, data privacy, cybersecurity, compliance, and enterprise risk management considerations. • Experience with modern cloud services, serverless architectures, Kubernetes, microservices, event-driven architecture, API management, observability platforms, and infrastructure-as-code. • Experience partnering with product, design, data, security, architecture, and business teams to identify high-impact use cases and deliver measurable business outcomes. • Proven ability to mentor engineers, influence technical direction without direct authority, and establish reusable engineering standards, patterns, frameworks, or accelerators. • Advanced degree, cloud certification, AI/ML certification, software architecture certification, or demonstrated contribution to technical communities, open-source projects, patents, publications, or internal innovation initiatives.

Job Description This is a technical presales role in an organisation that sells enterprise AI, data and analytics services at scale and differentiates on semantic and knowledge graph engineering. The person in this seat owns the technical side of the sales conversation in the US market: qualifying opportunities alongside the account team, shaping the solution architecture, running demonstrations and proofs of value on the Datavid accelerator platforms and writing the technical content of proposals and RFP responses. The role sits between the account team and delivery and is accountable both for the technical credibility of what is promised and for whether it can be built as described. About Us C5i is a global AI and analytics firm working with enterprise clients across life sciences, technology, financial services, industrials and consumer sectors. The group delivers data engineering, data science, augmented insights, enterprise generative AI services and competitive and market intelligence. Datavid, a C5i company, is the group's knowledge graph and semantic capability. Datavid designs ontology-driven data products, enriches data lakes and warehouses with a governed semantic layer, extracts entities and relationships from unstructured content, applies FAIR data principles and builds agentic and retrieval-augmented applications over graphs that can be audited and explained. What You Will Do Lead the client conversation. You will present to CIOs, CDOs, heads of R&D, enterprise architects and business sponsors, often in the same meeting, covering both the architecture and the business case. You will run discovery workshops, work through conflicting requirements between client teams, and answer detailed technical challenge from client architects. Qualify and shape opportunities. You will work alongside US sales leadership from first contact, joining discovery calls to establish what the prospect's data estate actually looks like, where meaning is currently lost and where can we help. Design the solution. You will produce the target architecture that sits behind each proposal: ontology and semantic model scope, ingestion and enrichment pipeline design, choice of graph and vector platform against the client's existing stack, retrieval and agentic patterns, and the governance model covering versioning, lineage, access control and audit. Demonstrate and prove. You will own Datavid accelerator platform demonstrations for the US market, tailoring them to the prospect's domain rather than showing a generic dataset, and you will scope and help run proofs of value and discovery sprints. Write the technical response. You will author the solution, architecture, security and delivery-approach content of RFI, RFP and proposal responses, working to the group's evidence discipline. Bridge to delivery. You will hand won work to the delivery organisation with an architecture, an assumptions register and a risk log that the delivery lead recognizes. Work as one team. You will work day to day with sales, delivery architects, engineering and bid colleagues across the US, Europe and India. Much of that work is shared, asynchronous and written, and the architecture you produce is reviewed by the people who will build it. Build the field's capability. You will feed reusable assets back into the library (demonstration scenarios, reference architectures, sector-specific answers, discovery question sets) and brief account teams so that routine technical questions stop reaching you. Where the group's partner relationships matter to an opportunity, you will support joint technical conversations with those partners. Essential Experience The requirements the hiring panel will not trade away are a genuine engineering background, working experience of knowledge graphs and semantic technology, and real client-facing experience in a presales or consulting setting. The remaining two are here because the role is unviable without them. Candidates should be ready to give specific examples against each. Technical depth, earned in build. You have worked as an engineer, data engineer, solution architect or equivalent, and you can still read and reason about a data pipeline, an API contract and a deployment topology. Knowledge graphs and semantics in practice. You have designed, built or implemented against ontologies and knowledge graphs on real projects (RDF, OWL, SHACL and SPARQL, or labelled property graphs and Cypher, or both). You understand taxonomy and ontology modelling, entity resolution, and what separates a governed semantic layer from a schema with a graph database behind it. Client-facing presales or consulting experience. Five or more years in a customer-facing presales, solution consulting or technical consulting role at a consultancy, systems integrator or technology services firm. You have led discovery sessions and workshops with enterprise clients, presented architecture to stakeholders who were not all on your side, and worked alongside a sales lead on qualification, scoping and pricing input. This is not a quota-carrying position, but you understand how a deal is put together and what makes one worth pursuing. Communication and interpersonal range. You can pitch the same material to an executive sponsor, a business owner and an enterprise architect. You work across functions and geographies in a matrixed organisation, and you can disagree with a client or a colleague and keep the relationship. Written technical persuasion. You can write a solution narrative an enterprise architect will respect and an executive sponsor will finish (proposals, RFP responses, architecture notes). Desirable Experience None of the following is a barrier to applying and no candidate is expected to hold all of them. Retrieval-augmented generation and agentic architectures: orchestration, planner and tool patterns, grounding, evaluation and guardrails. Hands-on familiarity with graph and search platforms such as Neo4j, Amazon Neptune, Stardog, GraphDB, MarkLogic, Elasticsearch or OpenSearch and with vector stores. Lakehouse and cloud data platforms: Databricks, Snowflake, AWS, Azure or GCP and how a semantic layer is positioned alongside them. Sector depth in life sciences and pharma, scientific publishing, medtech, financial services or manufacturing, particularly where regulatory traceability is a requirement. Master data management, data governance frameworks or FAIR data implementation. Experience presenting at orals or finalist sessions, and representing a firm at industry events, webinars or partner sessions. Working in a global delivery model with offshore engineering teams. Prior work in a services or consultancy environment where solutions were sold and then delivered by the same organisation. Selection Process Shortlisted candidates will be asked to provide a writing sample or complete a short written exercise, to present a solution to a simulated client panel, and to talk us through two or three opportunities they worked on, including one that did not go their way. C5i is proud to be an equal opportunity employer. We are committed to equal employment opportunity regardless of race, color, religion, sex, sexual orientation, age, marital status, disability, gender identity, etc. If you have a disability or special need that requires accommodation, please keep us informed about the same at the hiring stages for us to factor necessary accommodations.

Job Description Job Description Pay Rate Range: $ .78/hr. Job Description: The AI Operations Strategist is a strategic advisory role within the AI Office under the Digital Transformation Office (DTO). The role shapes the enterprise strategy for reliable, observable, cost-effective and value-driven AI operations The Strategist defines the enterprise direction for AIOps, AI observability, AI FinOps, operational governance, and value realization. The role establishes target-state capabilities, frameworks, standards, roadmaps, and success measures; aligns recommendations across the AI COE, Enterprise Architecture, Cybersecurity, and Information Governance; and enables ITSM, SIAM, AIOps, MLOps, Digital Platform, and delivery teams to implement, scale, and measure outcomes. Key Responsibilities 1. Enterprise AIOps Strategy •Define and evolve the enterprise AIOps vision, strategic principles, target-state capabilities and operating model. •Establish a multi-year AIOps capability roadmap aligned with SCE enterprise priorities and AI transformation objectives. •Assess current-state operational maturity, identify strategic capability gaps and recommend prioritized investments. •Define strategic patterns for predictive operations, anomaly detection, automated remediation and AI-enabled service management. •Guide ITSM, SRE, MLOps, Digital Platform and delivery teams in translating strategy into executable capabilities. 2. AI Observability and Telemetry Strategy •Define the enterprise AI observability and telemetry framework across model, platform, service, experience, risk and business-value dimensions. •Establish minimum observability standards, measurement principles and coverage expectations across the AI lifecycle. •Define enterprise KPIs and KRIs for AI reliability, performance, risk, adoption, cost and business outcomes. •Set strategic requirements for logging, tracing, telemetry, model-performance monitoring and operational insight generation. •Guide architecture, platform, MLOps and SRE teams to align implementation with approved enterprise standards. 3. AI FinOps and Cost Strategy •Define and govern the enterprise FinOps strategy for AI infrastructure, platforms, models, agents and shared AI services. •Establish AI cost-transparency principles, cost taxonomy, unit economics, allocation models and showback/chargeback strategy. •Define cost, consumption, optimization and value KPIs for enterprise AI capabilities. •Establish optimization principles and decision thresholds based on cost, performance, utilization, risk and business value. •Provide strategic forecasting, investment-planning and cost-optimization recommendations in partnership with Finance, Cloud, Platform and delivery teams. •Assess AI FinOps maturity and guide implementation without directly operating cost-management platforms or processes. 4. AI Operations Governance and Standards •Establish AI Operations governance, including decision rights, standards, review mechanisms, escalation principles and accountability boundaries. •Define operational-readiness criteria and governance checkpoints for AI solutions entering production. •Establish strategic standards for reliability, resilience, observability, service management and operational sustainability. •Orchestrate alignment across AI Office, AI COE, Enterprise Architecture, Cyber, Information Governance, ITSM, SIAM, SRE, MLOps, DAS, Digital Platform and Portfolio teams. •Identify ownership gaps, overlaps and dependencies and recommend appropriate governance interventions. 5. Value Realization and Executive Insights •Define the enterprise framework for measuring AI operational value, reliability, cost efficiency, adoption and business outcomes. •Establish standardized KPI, ROI and value-attribution principles across the AI portfolio. •Translate operational, financial and performance insights into strategic recommendations for AI Office and executive stakeholders. •Identify portfolio-level opportunities for investment, optimization, rationalization, reuse or capability improvement. •Provide executive insights on AI operations maturity, emerging risks, cost trends, value realization and strategic priorities. 6. Technology Strategy and Architecture Alignment •Define strategic requirements and evaluation criteria for AIOps, AI observability, telemetry and FinOps capabilities. •Assess technology options based on interoperability, scalability, architecture alignment, governance, cost and business value. •Recommend target-state capabilities and technology-direction principles in partnership with Enterprise Architecture and platform teams. •Promote standardization and reuse while reducing fragmented or overlapping operational capabilities. •Maintain technology neutrality and ensure recommended capabilities align with SCE-approved standards and ecosystem requirements. 7. Cross-Functional Orchestration •Serve as the strategic bridge between the AI Office and architecture, engineering, operations, finance, governance, cybersecurity and delivery stakeholders. •Translate AI strategy into clear operational principles, priorities and requirements for implementation teams. •Facilitate alignment on ownership, dependencies, standards, roadmaps and investment priorities. •Review implementation approaches against approved strategy and recommend corrective direction when required. •Incorporate operational lessons, market developments and stakeholder feedback into the evolving AI operations roadmap. Role Boundaries Strategist Accountabilities •Strategy, target state and capability roadmap •Frameworks, policies, principles, standards and guardrails •Portfolio-level KPI, FinOps, observability and value frameworks •Maturity assessment, governance and executive recommendations •Cross-functional orchestration and strategic implementation oversight Execution Owned by Delivery and Operations Teams •Tool configuration and administration •Telemetry integration, dashboard development and alert configuration •Production monitoring, incident resolution and automated remediation implementation •Cloud cost-management execution, model tuning and retraining •Day-to-day production support and platform operations Required Qualifications •10 years of experience across enterprise technology strategy, IT operations transformation, AIOps, SRE, cloud operations, observability or related disciplines. •Demonstrated experience defining enterprise strategies, operating models, governance frameworks, capability roadmaps and transformation programs. •Strong understanding of AI/ML operations, MLOps, model observability, reliability, telemetry and service management. •Strong understanding of FinOps principles, cloud economics, AI consumption models, cost allocation, forecasting and value optimization. •Experience translating technology, operational and financial insights into executive-level strategic recommendations. •Knowledge of enterprise cloud and AI ecosystems, including Microsoft Azure and Google Cloud Platform. •Knowledge of ITSM/ITIL, SRE, SLA/SLO/XLA, incident/problem management and operational resilience principles. •Strong strategic thinking, executive communication, facilitation and stakeholder-management capabilities. •Bachelor's degree in Computer Science, Information Technology, Engineering, Data Science, Business Technology or a related discipline. Preferred Qualifications •Experience defining enterprise AI operating models or AI transformation strategies in a complex, multi-provider environment. •Knowledge of leading AIOps and observability ecosystems such as Dynatrace, Splunk, Datadog, Arize.AI, Azure AI, GCP Vertex AI or equivalent platforms. •FinOps, ITIL4, Cloud, AI/ML, SRE or enterprise architecture certification. •Familiarity with Agentic AI, enterprise AI platforms, MLOps, Responsible AI and AI lifecycle governance. •Experience developing executive transformation roadmaps and portfolio-level value-realization frameworks. •Utility or energy-sector experience is preferred. Skills:CategoryNameRequiredImportanceExperienceSkillCategoryTest1_MNDigital : Microsoft AzureYes1>7 years

Job Description Job Description Job Title: Senior Application Architect - AI Agentic, Integration & Architecture COE Location: New York, NY (Hybrid - 3 days onsite and 2 days remote per week) Duration: 12 Months / CONTRACT NO BENEFITS Working Hours: 37.5 hours per week Position Level: Senior Level Certifications: Cloud certification and other relevant certifications preferred. Position Summary We are seeking a highly experienced Senior Application Architect with deep expertise in agentic AI architecture, enterprise integration, and application architecture standards. This is a hands-on senior-level role responsible for designing AI-enabled and agent-based solutions, defining enterprise integration patterns, establishing application architecture standards, and contributing to the development of an Architecture Center of Excellence (COE). The ideal candidate will have extensive experience designing enterprise-scale solutions across Microsoft Azure, AWS, and hybrid cloud environments. The role requires strong technical leadership, a solid understanding of AI/LLM architectures, and the ability to establish secure, scalable, and reusable architectural patterns across the enterprise. Key Responsibilities 1. AI Agentic Architecture Design reference architectures for agentic AI solutions, including multi-agent orchestration, tool calling, Retrieval-Augmented Generation (RAG), and human-in-the-loop controls. Define secure architectural patterns for integrating AI agents with enterprise systems, APIs, and services, including Model Context Protocol (MCP) and event-driven triggers. Establish governance guardrails for agent identity, access management, auditability, observability, evaluation, and cost optimization. Ensure AI-enabled solutions meet enterprise security, scalability, reliability, and governance requirements. Develop reusable architectural frameworks and best practices for enterprise AI adoption. 2. Enterprise Integration Architecture Design and govern API-led, event-driven, and messaging-based integration architectures. Define API lifecycle management strategies, API gateway standards, and enterprise integration patterns. Establish integration approaches for legacy applications, SaaS platforms, and cloud-native systems. Ensure integration architectures support interoperability, scalability, security, and maintainability. Promote reusable integration components and standardized architectural practices across enterprise applications. 3. Architecture Standards and Center of Excellence (COE) Develop, publish, and maintain application, integration, API, and AI architecture standards. Create reference architectures, technical guidelines, reusable design patterns, and implementation playbooks. Help establish and operate an Architecture Center of Excellence, including its charter, governance forums, design reviews, training programs, and operational processes. Define architecture governance processes to ensure consistency, compliance, and alignment with enterprise technology strategy. Establish metrics to measure architecture standards adoption and COE effectiveness in coordination with AI and IT adoption tracking initiatives. Provide architectural guidance and support to development teams to encourage adherence to established standards. 4. Application Modernization, Security, and Collaboration Guide application modernization initiatives and cloud architecture design across Azure, AWS, and hybrid environments. Ensure architectural designs align with applicable security frameworks and organizational requirements, including NIST, the NIST AI Risk Management Framework (AI RMF), CIS, ISO 27001, and relevant transportation-sector security requirements. Collaborate with product management, software engineering, DevOps, and Cybersecurity teams to deliver secure and scalable enterprise solutions. Evaluate emerging technologies and recommend architectural approaches that support business objectives and technology modernization. Communicate architectural decisions, technical strategies, and recommendations effectively to executive leadership, business stakeholders, and technical teams. Provide technical leadership throughout solution design, architecture reviews, and implementation activities. Required Qualifications 10+ years of experience in application architecture or enterprise architecture. Demonstrated experience designing cloud-native applications and enterprise solutions using Microsoft Azure and/or AWS. Hands-on experience delivering enterprise AI, Large Language Model (LLM), or agentic AI solutions. Strong understanding of agentic AI architecture, multi-agent orchestration, RAG, tool calling, and AI governance. Proven experience developing, documenting, and maintaining enterprise architecture standards and reference architectures. Experience establishing or operating an Architecture Center of Excellence (COE), architecture review board, or similar governance function. Extensive knowledge of enterprise integration architecture, API management, event-driven architecture, and messaging patterns. Understanding of application modernization strategies, hybrid cloud environments, and enterprise technology integration. Strong knowledge of security architecture, risk management, and enterprise governance frameworks. Excellent communication and stakeholder management skills, including the ability to present architectural strategies and recommendations to executive-level audiences. Technical Skills and Experience Complex Data Architecture: 6+ years of experience. Information Architecture: 6+ years of experience. IT Architecture: 6+ years of experience. Cloud Architecture: Experience with Azure, AWS, and/or hybrid cloud environments. AI and Agentic Architecture: Experience with enterprise AI/LLM solutions, multi-agent orchestration, RAG, and secure AI integrations. Enterprise Integration: Strong expertise in API-led integration, event-driven architecture, messaging, and API lifecycle management. Architecture Governance: Experience developing standards, reference architectures, governance frameworks, and COE operating models. Security and Compliance: Familiarity with NIST AI RMF, CIS, ISO 27001, and enterprise security requirements. Preferred Certifications Microsoft Azure or AWS cloud certification. Additional relevant certifications in enterprise architecture, cloud architecture, AI, cybersecurity, or integration architecture.

Job Description Job Description Karman Space & Defense is a leader in the rapid design, development, and production of critical, next-generation system solutions that align with the U.S. Department of War and its allies' core mission priorities, and meet the accelerating demand for access to space. Building on nearly 50 years of success, we deliver Payload & Protection Systems, Aero/Hydrodynamic Interstage Systems, and Propulsion & Launch Systems to more than 80 prime contractors supporting over 130 space and defense programs. In this senior individual contributor role, you will translate Karman's enterprise artificial intelligence (AI) strategy into secure, scalable, and reusable architectures that accelerate responsible adoption across the organization. You will shape technical patterns, build reference implementations, evaluate emerging technologies, and establish guardrails that enable governed self service. Partnering closely with AI/Data & Automation, Business Systems, Cybersecurity, Infrastructure, and functional teams, you will provide hands on technical leadership that strengthens solution quality, operational consistency, and enterprise readiness. Responsibilities Defines and evolves Karman's enterprise AI reference architecture. Establishes architectural patterns for model access, retrieval augmented generation (RAG), orchestration, agents, application programming interfaces (APIs), evaluation, observability, and cost management. Develops technical standards for Microsoft 365 Copilot, Copilot Studio, Azure AI Foundry, Azure OpenAI, Microsoft Fabric, and other approved enterprise AI capabilities. Evaluates external platforms and models based on security, performance, cost, and business needs. Designs architectures that support portability using APIs, abstraction layers, open protocols, and exit strategies. Builds proofs of concept, reference implementations, reusable components, and technical accelerators. Provides hands on support for complex AI use cases, including RAG solutions, agentic workflows, model integrated applications, and automation. Establishes reusable templates, patterns, guardrails, and documentation to enable governed self service. Translates enterprise AI governance requirements into technical controls for identity, access, logging, monitoring, classification, retention, and human oversight. Defines evaluation, observability, quality, operational readiness, incident response, and lifecycle management standards for AI solutions. Required Qualifications Significant experience in enterprise architecture, cloud architecture, software engineering, or platform engineering. Recent experience designing and deploying production generative artificial intelligence (AI) solutions. Strong working knowledge of the Microsoft cloud and AI ecosystem, including Microsoft 365 Copilot, Copilot Studio, Azure AI Foundry, Azure OpenAI, Microsoft Fabric, Entra ID, Purview, and Microsoft security technologies. Hands on experience integrating commercial AI models through APIs and building model enabled applications. Strong understanding of RAG architectures, agent patterns, tool use, model orchestration, vector search, and enterprise data integration. Familiarity with standards and technologies such as Model Context Protocol (MCP), OpenTelemetry, open weight models, and modern inference platforms. Experience designing secure systems in regulated or high assurance environments. Ability and willingness to write code, build prototypes, review designs, and troubleshoot technical issues. Strong written and verbal communication skills with the ability to explain complex technical tradeoffs to technical and executive audiences. Preferred Qualifications Experience in aerospace, defense, government, manufacturing, financial services, or other regulated industries. Experience with GCC High, sovereign cloud, restricted environments, or controlled data architectures. Familiarity with frameworks such as Cybersecurity Maturity Model Certification (CMMC), National Institute of Standards and Technology (NIST) Special Publication , Controlled Unclassified Information (CUI), International Traffic in Arms Regulations (ITAR), NIST AI Risk Management Framework (RMF), and International Organization for Standardization/International Electrotechnical Commission (ISO/IEC) 42001. Experience integrating AI with enterprise platforms such as enterprise resource planning (ERP), product lifecycle management (PLM), manufacturing execution systems (MES), customer relationship management (CRM), productivity, analytics, and workflow systems. Familiarity with graphics processing unit (GPU) infrastructure, self hosted models, fine tuning, and private inference environments. Relevant Microsoft, cloud, architecture, or security certifications. This position requires U.S. person status under U.S. export control laws, including U.S. citizens and nationals, lawful permanent residents, refugees, and asylees. Benefits Medical, dental, and vision insurance 401(k) with company match Paid time off Health Savings Account (HSA) with company contribution Flexible Spending Accounts (FSA) Company paid life and AD &D insurance Short and long term disability coverage Tuition reimbursement Karman Space and Defense is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, age, disability, genetic information, protected veteran status, or any other status protected by applicable law.

Job Description Job Description Job Title: Senior Application Architect - AI Agentic, Integration & Architecture COE Location: New York, NY (Hybrid - 3 days onsite and 2 days remote per week) Duration: 12 Months / CONTRACT NO BENEFITS Working Hours: 37.5 hours per week Position Level: Senior Level Certifications: Cloud certification and other relevant certifications preferred. Position Summary We are seeking a highly experienced Senior Application Architect with deep expertise in agentic AI architecture, enterprise integration, and application architecture standards. This is a hands-on senior-level role responsible for designing AI-enabled and agent-based solutions, defining enterprise integration patterns, establishing application architecture standards, and contributing to the development of an Architecture Center of Excellence (COE). The ideal candidate will have extensive experience designing enterprise-scale solutions across Microsoft Azure, AWS, and hybrid cloud environments. The role requires strong technical leadership, a solid understanding of AI/LLM architectures, and the ability to establish secure, scalable, and reusable architectural patterns across the enterprise. Key Responsibilities 1. AI Agentic Architecture Design reference architectures for agentic AI solutions, including multi-agent orchestration, tool calling, Retrieval-Augmented Generation (RAG), and human-in-the-loop controls. Define secure architectural patterns for integrating AI agents with enterprise systems, APIs, and services, including Model Context Protocol (MCP) and event-driven triggers. Establish governance guardrails for agent identity, access management, auditability, observability, evaluation, and cost optimization. Ensure AI-enabled solutions meet enterprise security, scalability, reliability, and governance requirements. Develop reusable architectural frameworks and best practices for enterprise AI adoption. 2. Enterprise Integration Architecture Design and govern API-led, event-driven, and messaging-based integration architectures. Define API lifecycle management strategies, API gateway standards, and enterprise integration patterns. Establish integration approaches for legacy applications, SaaS platforms, and cloud-native systems. Ensure integration architectures support interoperability, scalability, security, and maintainability. Promote reusable integration components and standardized architectural practices across enterprise applications. 3. Architecture Standards and Center of Excellence (COE) Develop, publish, and maintain application, integration, API, and AI architecture standards. Create reference architectures, technical guidelines, reusable design patterns, and implementation playbooks. Help establish and operate an Architecture Center of Excellence, including its charter, governance forums, design reviews, training programs, and operational processes. Define architecture governance processes to ensure consistency, compliance, and alignment with enterprise technology strategy. Establish metrics to measure architecture standards adoption and COE effectiveness in coordination with AI and IT adoption tracking initiatives. Provide architectural guidance and support to development teams to encourage adherence to established standards. 4. Application Modernization, Security, and Collaboration Guide application modernization initiatives and cloud architecture design across Azure, AWS, and hybrid environments. Ensure architectural designs align with applicable security frameworks and organizational requirements, including NIST, the NIST AI Risk Management Framework (AI RMF), CIS, ISO 27001, and relevant transportation-sector security requirements. Collaborate with product management, software engineering, DevOps, and Cybersecurity teams to deliver secure and scalable enterprise solutions. Evaluate emerging technologies and recommend architectural approaches that support business objectives and technology modernization. Communicate architectural decisions, technical strategies, and recommendations effectively to executive leadership, business stakeholders, and technical teams. Provide technical leadership throughout solution design, architecture reviews, and implementation activities. Required Qualifications 10+ years of experience in application architecture or enterprise architecture. Demonstrated experience designing cloud-native applications and enterprise solutions using Microsoft Azure and/or AWS. Hands-on experience delivering enterprise AI, Large Language Model (LLM), or agentic AI solutions. Strong understanding of agentic AI architecture, multi-agent orchestration, RAG, tool calling, and AI governance. Proven experience developing, documenting, and maintaining enterprise architecture standards and reference architectures. Experience establishing or operating an Architecture Center of Excellence (COE), architecture review board, or similar governance function. Extensive knowledge of enterprise integration architecture, API management, event-driven architecture, and messaging patterns. Understanding of application modernization strategies, hybrid cloud environments, and enterprise technology integration. Strong knowledge of security architecture, risk management, and enterprise governance frameworks. Excellent communication and stakeholder management skills, including the ability to present architectural strategies and recommendations to executive-level audiences. Technical Skills and Experience Complex Data Architecture: 6+ years of experience. Information Architecture: 6+ years of experience. IT Architecture: 6+ years of experience. Cloud Architecture: Experience with Azure, AWS, and/or hybrid cloud environments. AI and Agentic Architecture: Experience with enterprise AI/LLM solutions, multi-agent orchestration, RAG, and secure AI integrations. Enterprise Integration: Strong expertise in API-led integration, event-driven architecture, messaging, and API lifecycle management. Architecture Governance: Experience developing standards, reference architectures, governance frameworks, and COE operating models. Security and Compliance: Familiarity with NIST AI RMF, CIS, ISO 27001, and enterprise security requirements. Preferred Certifications Microsoft Azure or AWS cloud certification. Additional relevant certifications in enterprise architecture, cloud architecture, AI, cybersecurity, or integration architecture.

Job Description Job Description Position: AWS AI Senior Tech Lead Location: Remote (Charlotte, NC/Jersey City, NJ) Duration: Full-time Salary Range: $130,000-$150,000 /Annum Job Description AWS Certified Solutions Architect Professional or equivalent cloud architecture certification preferred. Strong hands-on capability in Python, TypeScript/JavaScript, REST APIs, microservices, event-driven patterns, and containerized deployments. Experience with AWS Bedrock, SageMaker, Lambda, Step Functions, ECS/EKS, API Gateway, S3, CloudWatch, and CI/CD tooling. Deep understanding of prompt engineering, agent orchestration, tool integration, vector stores, embeddings, model evaluation, and AI guardrails. Ability to enforce engineering standards, code quality, secure coding, performance tuning, and observability practices. Strong leadership, mentoring, estimation, planning, and stakeholder communication skills. Familiarity with compliance, audit logging, model risk controls, and responsible AI practices Strong understanding of AI agent patterns, prompt orchestration, tool/function calling, RAG pipelines, model evaluation, guardrails, and observability. Strong application security and cloud security knowledge, including threat modeling, OWASP/API security, identity and access management, encryption, secure SDLC, logging, monitoring, and resilience patterns. Ability to define non-functional requirements for security, privacy, performance, resiliency, scalability, auditability, and operational readiness. Excellent communication skills with ability to present architecture decisions, risks, and trade-offs to senior stakeholders. Roles & Responsibilities Lead the technical delivery of AI agent capabilities, reusable platform components, integrations, and automation workflows. Translate solution architecture into detailed engineering designs, sprint plans, and implementation tasks. Guide developers on AI patterns, cloud-native implementation, secure coding, testability, and performance Eligible to workimization. Build and review core components such as agent orchestration services, API layers, RAG services, tool connectors, and model interaction modules. Coordinate with architects, product owners, UX designers, security SMEs, and QA teams to drive end-to-end delivery quality. Establish technical best practices, coding standards, review processes, and reusable accelerators. Support environment readiness, deployment planning, defect triage, production readiness reviews, and knowledge transition. Collaborate with enterprise architecture, security, data, cloud platform, application, and business teams to ensure alignment with client standards. Embed application security and cloud security requirements into solution design, including IAM, encryption, network controls, API protection, secrets management, secure data access, logging, and monitoring. Drive build-versus-buy evaluations, technology selection, platform design decisions, security trade-off analysis, and architectural risk mitigation. Establish reusable cloud patterns, secure integration standards, deployment approaches, and non-functional requirements for performance, resiliency, and security. Review detailed designs and implementation outputs to ensure adherence to approved architecture, security controls, and operational readiness expectations. Generic Managerial Skills, If any Bachelor s degree in computer science, Engineering, Information Systems, Cybersecurity, or related field; advanced degree preferred. 10 years of technology architecture experience, including 4 years designing cloud-native solutions on AWS. Strong experience architecting Gen AI, LLM, RAG, agentic workflow, and AI platform solutions for enterprise-scale programs. Hands-on exposure to AWS Bedrock, SageMaker, Lambda, Step Functions, API Gateway, ECS/EKS, S3, IAM, CloudWatch, networking, and security constructs. Strong understanding of application security and cloud security, including secure design, IAM, encryption, network segmentation, API security, secrets management, logging, monitoring, and compliance controls. Experience designing governed, compliant, auditable platforms for regulated domains; insurance or financial services experience preferred

Job Description Job Description Location: Rosemead, CA Duration: 6 months GBaMS ReqID: Position Summary: The AI Operations Strategist is a strategic advisory role within the AI Office under the Digital Transformation Office (DTO). The role shapes the enterprise strategy for reliable, observable, cost-effective and value-driven AI operations The Strategist defines the enterprise direction for AIOps, AI observability, AI FinOps, operational governance, and value realization. The role establishes target-state capabilities, frameworks, standards, roadmaps, and success measures; aligns recommendations across the AI COE, Enterprise Architecture, Cybersecurity, and Information Governance; and enables ITSM, SIAM, AIOps, MLOps, Digital Platform, and delivery teams to implement, scale, and measure outcomes. Key Responsibilities 1. Enterprise AIOps Strategy •Define and evolve the enterprise AIOps vision, strategic principles, target-state capabilities and operating model. •Establish a multi-year AIOps capability roadmap aligned with SCE enterprise priorities and AI transformation objectives. •Assess current-state operational maturity, identify strategic capability gaps and recommend prioritized investments. •Define strategic patterns for predictive operations, anomaly detection, automated remediation and AI-enabled service management. •Guide ITSM, SRE, MLOps, Digital Platform and delivery teams in translating strategy into executable capabilities. 2. AI Observability and Telemetry Strategy •Define the enterprise AI observability and telemetry framework across model, platform, service, experience, risk and business-value dimensions. •Establish minimum observability standards, measurement principles and coverage expectations across the AI lifecycle. •Define enterprise KPIs and KRIs for AI reliability, performance, risk, adoption, cost and business outcomes. •Set strategic requirements for logging, tracing, telemetry, model-performance monitoring and operational insight generation. •Guide architecture, platform, MLOps and SRE teams to align implementation with approved enterprise standards. 3. AI FinOps and Cost Strategy •Define and govern the enterprise FinOps strategy for AI infrastructure, platforms, models, agents and shared AI services. •Establish AI cost-transparency principles, cost taxonomy, unit economics, allocation models and showback/chargeback strategy. •Define cost, consumption, optimization and value KPIs for enterprise AI capabilities. •Establish optimization principles and decision thresholds based on cost, performance, utilization, risk and business value. •Provide strategic forecasting, investment-planning and cost-optimization recommendations in partnership with Finance, Cloud, Platform and delivery teams. •Assess AI FinOps maturity and guide implementation without directly operating cost-management platforms or processes. 4. AI Operations Governance and Standards •Establish AI Operations governance, including decision rights, standards, review mechanisms, escalation principles and accountability boundaries. •Define operational-readiness criteria and governance checkpoints for AI solutions entering production. •Establish strategic standards for reliability, resilience, observability, service management and operational sustainability. •Orchestrate alignment across AI Office, AI COE, Enterprise Architecture, Cyber, Information Governance, ITSM, SIAM, SRE, MLOps, DAS, Digital Platform and Portfolio teams. •Identify ownership gaps, overlaps and dependencies and recommend appropriate governance interventions. 5. Value Realization and Executive Insights •Define the enterprise framework for measuring AI operational value, reliability, cost efficiency, adoption and business outcomes. •Establish standardized KPI, ROI and value-attribution principles across the AI portfolio. •Translate operational, financial and performance insights into strategic recommendations for AI Office and executive stakeholders. •Identify portfolio-level opportunities for investment, optimization, rationalization, reuse or capability improvement. •Provide executive insights on AI operations maturity, emerging risks, cost trends, value realization and strategic priorities. 6. Technology Strategy and Architecture Alignment •Define strategic requirements and evaluation criteria for AIOps, AI observability, telemetry and FinOps capabilities. •Assess technology options based on interoperability, scalability, architecture alignment, governance, cost and business value. •Recommend target-state capabilities and technology-direction principles in partnership with Enterprise Architecture and platform teams. •Promote standardization and reuse while reducing fragmented or overlapping operational capabilities. •Maintain technology neutrality and ensure recommended capabilities align with SCE-approved standards and ecosystem requirements. 7. Cross-Functional Orchestration •Serve as the strategic bridge between the AI Office and architecture, engineering, operations, finance, governance, cybersecurity and delivery stakeholders. •Translate AI strategy into clear operational principles, priorities and requirements for implementation teams. •Facilitate alignment on ownership, dependencies, standards, roadmaps and investment priorities. •Review implementation approaches against approved strategy and recommend corrective direction when required. •Incorporate operational lessons, market developments and stakeholder feedback into the evolving AI operations roadmap. Role Boundaries Strategist Accountabilities •Strategy, target state and capability roadmap •Frameworks, policies, principles, standards and guardrails •Portfolio-level KPI, FinOps, observability and value frameworks •Maturity assessment, governance and executive recommendations •Cross-functional orchestration and strategic implementation oversight Execution Owned by Delivery and Operations Teams •Tool configuration and administration •Telemetry integration, dashboard development and alert configuration •Production monitoring, incident resolution and automated remediation implementation •Cloud cost-management execution, model tuning and retraining •Day-to-day production support and platform operations Required Qualifications •10+ years of experience across enterprise technology strategy, IT operations transformation, AIOps, SRE, cloud operations, observability or related disciplines. •Demonstrated experience defining enterprise strategies, operating models, governance frameworks, capability roadmaps and transformation programs. •Strong understanding of AI/ML operations, MLOps, model observability, reliability, telemetry and service management. •Strong understanding of FinOps principles, cloud economics, AI consumption models, cost allocation, forecasting and value optimization. •Experience translating technology, operational and financial insights into executive-level strategic recommendations. •Knowledge of enterprise cloud and AI ecosystems, including Microsoft Azure and Google Cloud Platform. •Knowledge of ITSM/ITIL, SRE, SLA/SLO/XLA, incident/problem management and operational resilience principles. •Strong strategic thinking, executive communication, facilitation and stakeholder-management capabilities. •Bachelor's degree in Computer Science, Information Technology, Engineering, Data Science, Business Technology or a related discipline. Preferred Qualifications •Experience defining enterprise AI operating models or AI transformation strategies in a complex, multi-provider environment. •Knowledge of leading AIOps and observability ecosystems such as Dynatrace, Splunk, Datadog, Arize.AI, Azure AI, GCP Vertex AI or equivalent platforms. •FinOps, ITIL4, Cloud, AI/ML, SRE or enterprise architecture certification. •Familiarity with Agentic AI, enterprise AI platforms, MLOps, Responsible AI and AI lifecycle governance. •Experience developing executive transformation roadmaps and portfolio-level value-realization frameworks. •Utility or energy-sector experience is preferred.

Job Description Job Description We are looking for an experienced AI Application Architect to lead the technical design and evolution of our AI-powered product suite. You will be the technical authority on how we build, scale, and secure AI-native applications - spanning agent orchestration, LLM infrastructure, data pipelines, and developer tooling. You'll work closely with engineering leads, product managers, and ML practitioners to translate ambitious AI capabilities into robust, production-grade systems. What You'll Do: Define architecture for AI-native applications including agentic systems, RAG pipelines, multi-model inference layers, and human-in-the-loop workflows Drive infrastructure decisions for scalable AI workloads: vector databases (Turbopuffer, pgvector, Milvus), workflow orchestration (Temporal, Airflow), and async compute patterns Design and govern the integration layer between LLMs (OpenAI, Anthropic, Gemini, open-source) and backend services, including prompt management, context window optimization, and cost governance Lead LLMOps and observability strategy - tracing (OpenTelemetry), evaluation pipelines, prompt versioning, drift detection, and integration with platforms like Langfuse or Arize Establish security and compliance posture for AI systems - SSRF/injection hardening, LLM guardrails, data residency, and supply chain security for model artifacts and dependencies Partner with product and research to evaluate emerging AI capabilities and determine when/how to adopt them (e.g., reasoning models, multimodal, fine-tuning, RLHF) Champion engineering standards - API design, schema governance, testing strategies, and architecture decision records (ADRs) Mentor senior engineers and establish guild-level technical communities around AI platform topics What We're Looking For: 10+ years of software engineering with 5+ years in a principal/staff or architect-level role Hands-on experience designing and shipping production LLM-powered applications (not just prototypes) Experience shipping complex, user-facing application platforms - particularly in SaaS, productivity tools, or enterprise software. Demonstrated ability to lead full-stack teams building rich web application experiences (React, TypeScript/Node.js, modern frontend architectures) with strong backend (Python) and API design sensibility Deep understanding of enterprise requirements: User Management, RBAC/ABAC permission models, audit logging, compliance frameworks, multi-tenant governance, and admin tooling. Experience building contextual or adaptive UX - applications that respond to user state, workflow context, or personalization signals. Strong grasp of cloud-native architecture (Kubernetes, Helm, container security, CI/CD) on AWS, GCP, or Azure Solid understanding of API security: OAuth2, CSRF, rate limiting, secrets management, and zero-trust principles applied to AI endpoints Preferred Experience: Experience with MCP (Model Context Protocol) or comparable tool-calling/plugin infrastructure Familiarity with workflow orchestration engines (Temporal, Prefect) for long-running AI tasks Exposure to LLM evaluation frameworks (automated judging, red-teaming, regression suites) Background in developer-facing products or internal AI platforms / AI coding tooling Understanding of supply chain security for ML models (artifact signing, registry enforcement, SBOM) Contributions to open-source AI tooling or published architectural writing Additional Information Base Pay Range: 233 870.00 USD Annual This role may also be eligible for Equity Compensation and/or Bonus Incentive Compensation. Procore is committed to offering competitive, fair, and commensurate compensation. Actual compensation will be based on a candidate's job-related skills, experience, education or training, and location. For Los Angeles County (unincorporated) Candidates: Procore will consider for employment all qualified applicants, including those with arrest or conviction records, in accordance with the requirements of applicable federal, state, and local laws, including the City of Los Angeles' Fair Chance Initiative for Hiring Ordinance, the Los Angeles County Fair Chance Ordinance for Employers, and the California Fair Chance Act. A criminal history may have a direct, adverse, and negative relationship on the following job duties, potentially resulting in the withdrawal of the conditional offer of employment: 1. appropriately managing, accessing, and handling confidential information including proprietary and trade secret information, as well as accessing Procore's information technology systems and platforms; 2. interacting with and occasionally having unsupervised contact with internal/external customers, stakeholders, and/or colleagues; and 3. exercising sound judgment.

Job Description Job Description Empowered to live. Inspired to work. Compeer Financial is a member-owned cooperative located in Illinois, Minnesota and Wisconsin. We bring together team members with a variety of backgrounds and experiences to help provide financial services to support agriculture and rural communities. Join us in a culture that not only promotes meaningful work and professional development, but provides a flexible, hybrid work environment and excellent benefits, which empower you to thrive both personally and professionally. How we support you: Hybrid model - up to 50% work from home Flexible schedules including ample flexibility in the summer months Up to 9% towards 401k (3% fixed Compeer contribution plus up to 6% match) Benefits: medical, dental, vision, HSA/FSA, life & AD&D insurance, short-term and long-term disability, wellness program & EAP Vacation, sick leave, holidays/floating holidays, parental leave, and volunteer paid time off Learning and development programs Mentorship programs Cross-functional committee opportunities (i.e. Inclusion Council, emerging professional groups, etc.) Professional membership/certification reimbursement and more! Casual/seasonal & intern team members are not eligible for benefits except for state-mandated programs. To learn more about Compeer Financial visit Where you will work: This position offers a hybrid work option up to 50% and is open to any Compeer office location in Illinois, Minnesota and Wisconsin. The contributions you will make: The highest-level individual contributor in the software engineering career framework, providing enterprise technical leadership across portfolios, products, platforms, and transformation initiatives, with a focus on leading and expanding responsible AI use across the organization. Sets enterprise AI Enablement Platform direction and partnering with Enterprise Architecture and AI Risk governance workgroup. Connects AI technology platform strategy to enterprise needs, risks, and measurable enterprise value. Advances AI-enabled engineering and Agentic AI at scale, building tools and platforms that make AI adoption safe, repeatable, and accessible. Serves as a trusted advisor to senior technology and business leaders and models the behaviors that strengthen Compeer's engineering culture. A typical day: Leads AI platform engineering and adoption by building enterprise AI platforms, tools, services, and integrations for LLMs, retrieval-augmented generation, vector search, agents, Model Context Protocol, and AI-assisted and agentic software development. Establishes AI operational excellence through reusable components and guardrails, evaluation frameworks, monitoring, cost and performance management, output validation, production support, and responsible scaling of AI tools and agents. Partners on AI governance and risk with Risk Management, Information Security, Legal, Compliance, and Data Governance, translating requirements into access controls, data protection, model and vendor risk management, human oversight, auditability, and use-case risk tiering. Owns AI-enabled and Agentic AI engineering delivery for the most strategic and ambiguous software initiatives from discovery through sustained operation, making significant cross-boundary technical decisions and remaining accountable for quality, security, reliability, scalability, and production performance. Develops engineers and technical leaders through mentoring, communities of practice, training, and reusable guidance on AI-assisted development, prompt and context engineering, responsible AI, agentic development, and critical evaluation of AI-generated output. Advances technical mastery and engineering excellence across architecture, distributed systems, integration, cloud-native engineering, DevSecOps, testing, CI/CD, security, observability, and reliability while evaluating emerging technologies through experimentation and evidence. Connects engineering to business outcomes by translating Compeer's strategy, client needs, regulatory obligations, and risk into engineering strategies, investment options, build-versus-buy decisions, high-value AI opportunities, and multi-year roadmaps. Leads across organizational boundaries by partnering with engineering, architecture, product, security, data, operations, quality, risk, and business leaders Influences without authority, resolving conflicts and dependencies, and communicating risks and trade-offs clearly. The skills and experience we prefer you have: Bachelor's degree in Computer Science, Software Engineering, or related discipline, or equivalent experience, with extensive experience designing, building, and operating complex, business-critical software, distributed systems, and platforms. Hands-on experience designing and building AI applications and platforms using LLMs, retrieval-augmented generation, vector search, agent orchestration, Model Context Protocol, and cloud AI services such as Azure OpenAI or Azure AI Foundry. Experience scaling responsible AI-enabled engineering and Agentic AI across the software lifecycle, prompt and model versioning, quality and safety testing, cost and performance monitoring, production support, and validation of AI-generated outcomes. Working knowledge of responsible AI and AI governance, model and vendor risk management, privacy, bias and fairness, auditability, human oversight, sensitive-data protection, and translating governance requirements into practical technical controls; experience in financial services, agriculture, or another regulated industry is preferred. Experience leading complex solution design and strategic technical decisions across multiple portfolios, products, systems, or business domains, including architecture, integration, technology trade-offs, engineering strategy, standards, governance, and target-state architecture. Distinguished software engineering expertise across application development, APIs and integration, cloud-native and event-driven architectures, data, security, platforms, DevSecOps, testing, CI/CD, observability, reliability, and operational excellence. Ability to analyze highly complex problems, anticipate second- and third-order effects, balance technical, security, regulatory, operational, and investment trade-offs, and connect engineering decisions to Compeer's strategy, client needs, enterprise value, and durable multi-year direction. Exceptional communication, influence, leadership, and learning agility, including the ability to advise executives and governance bodies, constructively challenge assumptions, resolve cross-organizational dependencies, mentor senior technical talent, and rapidly evaluate and responsibly adopt emerging technologies. How we will take care of you: Our job titles may span more than one career level (associate, senior, principal, etc.). The actual title and base pay offered is dependent upon many factors, such as: training, transferable skills, work experience, business needs and market demands. The base pay range is subject to change and may be modified in the future. This role is eligible for variable compensation and other benefits. Base Pay $133,000-$180,000 USD Compeer Financial is an equal opportunity employer and all qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, disability status, protected veteran status, or any other characteristic protected by law. Must be authorized to work for any employer in the United States. Compeer is unable to sponsor or take over sponsorship of an employment visa at this time. Click here to view federal employment laws applicable for applicants.

Job Description Job Description Empowered to live. Inspired to work. Compeer Financial is a member-owned cooperative located in Illinois, Minnesota and Wisconsin. We bring together team members with a variety of backgrounds and experiences to help provide financial services to support agriculture and rural communities. Join us in a culture that not only promotes meaningful work and professional development, but provides a flexible, hybrid work environment and excellent benefits, which empower you to thrive both personally and professionally. How we support you: Hybrid model - up to 50% work from home Flexible schedules including ample flexibility in the summer months Up to 9% towards 401k (3% fixed Compeer contribution plus up to 6% match) Benefits: medical, dental, vision, HSA/FSA, life & AD&D insurance, short-term and long-term disability, wellness program & EAP Vacation, sick leave, holidays/floating holidays, parental leave, and volunteer paid time off Learning and development programs Mentorship programs Cross-functional committee opportunities (i.e. Inclusion Council, emerging professional groups, etc.) Professional membership/certification reimbursement and more! Casual/seasonal & intern team members are not eligible for benefits except for state-mandated programs. To learn more about Compeer Financial visit Where you will work: This position offers a hybrid work option up to 50% and is open to any Compeer office location in Illinois, Minnesota and Wisconsin. The contributions you will make: The highest-level individual contributor in the software engineering career framework, providing enterprise technical leadership across portfolios, products, platforms, and transformation initiatives, with a focus on leading and expanding responsible AI use across the organization. Sets enterprise AI Enablement Platform direction and partnering with Enterprise Architecture and AI Risk governance workgroup. Connects AI technology platform strategy to enterprise needs, risks, and measurable enterprise value. Advances AI-enabled engineering and Agentic AI at scale, building tools and platforms that make AI adoption safe, repeatable, and accessible. Serves as a trusted advisor to senior technology and business leaders and models the behaviors that strengthen Compeer's engineering culture. A typical day: Leads AI platform engineering and adoption by building enterprise AI platforms, tools, services, and integrations for LLMs, retrieval-augmented generation, vector search, agents, Model Context Protocol, and AI-assisted and agentic software development. Establishes AI operational excellence through reusable components and guardrails, evaluation frameworks, monitoring, cost and performance management, output validation, production support, and responsible scaling of AI tools and agents. Partners on AI governance and risk with Risk Management, Information Security, Legal, Compliance, and Data Governance, translating requirements into access controls, data protection, model and vendor risk management, human oversight, auditability, and use-case risk tiering. Owns AI-enabled and Agentic AI engineering delivery for the most strategic and ambiguous software initiatives from discovery through sustained operation, making significant cross-boundary technical decisions and remaining accountable for quality, security, reliability, scalability, and production performance. Develops engineers and technical leaders through mentoring, communities of practice, training, and reusable guidance on AI-assisted development, prompt and context engineering, responsible AI, agentic development, and critical evaluation of AI-generated output. Advances technical mastery and engineering excellence across architecture, distributed systems, integration, cloud-native engineering, DevSecOps, testing, CI/CD, security, observability, and reliability while evaluating emerging technologies through experimentation and evidence. Connects engineering to business outcomes by translating Compeer's strategy, client needs, regulatory obligations, and risk into engineering strategies, investment options, build-versus-buy decisions, high-value AI opportunities, and multi-year roadmaps. Leads across organizational boundaries by partnering with engineering, architecture, product, security, data, operations, quality, risk, and business leaders Influences without authority, resolving conflicts and dependencies, and communicating risks and trade-offs clearly. The skills and experience we prefer you have: Bachelor's degree in Computer Science, Software Engineering, or related discipline, or equivalent experience, with extensive experience designing, building, and operating complex, business-critical software, distributed systems, and platforms. Hands-on experience designing and building AI applications and platforms using LLMs, retrieval-augmented generation, vector search, agent orchestration, Model Context Protocol, and cloud AI services such as Azure OpenAI or Azure AI Foundry. Experience scaling responsible AI-enabled engineering and Agentic AI across the software lifecycle, prompt and model versioning, quality and safety testing, cost and performance monitoring, production support, and validation of AI-generated outcomes. Working knowledge of responsible AI and AI governance, model and vendor risk management, privacy, bias and fairness, auditability, human oversight, sensitive-data protection, and translating governance requirements into practical technical controls; experience in financial services, agriculture, or another regulated industry is preferred. Experience leading complex solution design and strategic technical decisions across multiple portfolios, products, systems, or business domains, including architecture, integration, technology trade-offs, engineering strategy, standards, governance, and target-state architecture. Distinguished software engineering expertise across application development, APIs and integration, cloud-native and event-driven architectures, data, security, platforms, DevSecOps, testing, CI/CD, observability, reliability, and operational excellence. Ability to analyze highly complex problems, anticipate second- and third-order effects, balance technical, security, regulatory, operational, and investment trade-offs, and connect engineering decisions to Compeer's strategy, client needs, enterprise value, and durable multi-year direction. Exceptional communication, influence, leadership, and learning agility, including the ability to advise executives and governance bodies, constructively challenge assumptions, resolve cross-organizational dependencies, mentor senior technical talent, and rapidly evaluate and responsibly adopt emerging technologies. How we will take care of you: Our job titles may span more than one career level (associate, senior, principal, etc.). The actual title and base pay offered is dependent upon many factors, such as: training, transferable skills, work experience, business needs and market demands. The base pay range is subject to change and may be modified in the future. This role is eligible for variable compensation and other benefits. Base Pay $133,000-$180,000 USD Compeer Financial is an equal opportunity employer and all qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, disability status, protected veteran status, or any other characteristic protected by law. Must be authorized to work for any employer in the United States. Compeer is unable to sponsor or take over sponsorship of an employment visa at this time. Click here to view federal employment laws applicable for applicants.

Job Description Job Description Empowered to live. Inspired to work. Compeer Financial is a member-owned cooperative located in Illinois, Minnesota and Wisconsin. We bring together team members with a variety of backgrounds and experiences to help provide financial services to support agriculture and rural communities. Join us in a culture that not only promotes meaningful work and professional development, but provides a flexible, hybrid work environment and excellent benefits, which empower you to thrive both personally and professionally. How we support you: Hybrid model - up to 50% work from home Flexible schedules including ample flexibility in the summer months Up to 9% towards 401k (3% fixed Compeer contribution plus up to 6% match) Benefits: medical, dental, vision, HSA/FSA, life & AD&D insurance, short-term and long-term disability, wellness program & EAP Vacation, sick leave, holidays/floating holidays, parental leave, and volunteer paid time off Learning and development programs Mentorship programs Cross-functional committee opportunities (i.e. Inclusion Council, emerging professional groups, etc.) Professional membership/certification reimbursement and more! Casual/seasonal & intern team members are not eligible for benefits except for state-mandated programs. To learn more about Compeer Financial visit Where you will work: This position offers a hybrid work option up to 50% and is open to any Compeer office location in Illinois, Minnesota and Wisconsin. The contributions you will make: The highest-level individual contributor in the software engineering career framework, providing enterprise technical leadership across portfolios, products, platforms, and transformation initiatives, with a focus on leading and expanding responsible AI use across the organization. Sets enterprise AI Enablement Platform direction and partnering with Enterprise Architecture and AI Risk governance workgroup. Connects AI technology platform strategy to enterprise needs, risks, and measurable enterprise value. Advances AI-enabled engineering and Agentic AI at scale, building tools and platforms that make AI adoption safe, repeatable, and accessible. Serves as a trusted advisor to senior technology and business leaders and models the behaviors that strengthen Compeer's engineering culture. A typical day: Leads AI platform engineering and adoption by building enterprise AI platforms, tools, services, and integrations for LLMs, retrieval-augmented generation, vector search, agents, Model Context Protocol, and AI-assisted and agentic software development. Establishes AI operational excellence through reusable components and guardrails, evaluation frameworks, monitoring, cost and performance management, output validation, production support, and responsible scaling of AI tools and agents. Partners on AI governance and risk with Risk Management, Information Security, Legal, Compliance, and Data Governance, translating requirements into access controls, data protection, model and vendor risk management, human oversight, auditability, and use-case risk tiering. Owns AI-enabled and Agentic AI engineering delivery for the most strategic and ambiguous software initiatives from discovery through sustained operation, making significant cross-boundary technical decisions and remaining accountable for quality, security, reliability, scalability, and production performance. Develops engineers and technical leaders through mentoring, communities of practice, training, and reusable guidance on AI-assisted development, prompt and context engineering, responsible AI, agentic development, and critical evaluation of AI-generated output. Advances technical mastery and engineering excellence across architecture, distributed systems, integration, cloud-native engineering, DevSecOps, testing, CI/CD, security, observability, and reliability while evaluating emerging technologies through experimentation and evidence. Connects engineering to business outcomes by translating Compeer's strategy, client needs, regulatory obligations, and risk into engineering strategies, investment options, build-versus-buy decisions, high-value AI opportunities, and multi-year roadmaps. Leads across organizational boundaries by partnering with engineering, architecture, product, security, data, operations, quality, risk, and business leaders Influences without authority, resolving conflicts and dependencies, and communicating risks and trade-offs clearly. The skills and experience we prefer you have: Bachelor's degree in Computer Science, Software Engineering, or related discipline, or equivalent experience, with extensive experience designing, building, and operating complex, business-critical software, distributed systems, and platforms. Hands-on experience designing and building AI applications and platforms using LLMs, retrieval-augmented generation, vector search, agent orchestration, Model Context Protocol, and cloud AI services such as Azure OpenAI or Azure AI Foundry. Experience scaling responsible AI-enabled engineering and Agentic AI across the software lifecycle, prompt and model versioning, quality and safety testing, cost and performance monitoring, production support, and validation of AI-generated outcomes. Working knowledge of responsible AI and AI governance, model and vendor risk management, privacy, bias and fairness, auditability, human oversight, sensitive-data protection, and translating governance requirements into practical technical controls; experience in financial services, agriculture, or another regulated industry is preferred. Experience leading complex solution design and strategic technical decisions across multiple portfolios, products, systems, or business domains, including architecture, integration, technology trade-offs, engineering strategy, standards, governance, and target-state architecture. Distinguished software engineering expertise across application development, APIs and integration, cloud-native and event-driven architectures, data, security, platforms, DevSecOps, testing, CI/CD, observability, reliability, and operational excellence. Ability to analyze highly complex problems, anticipate second- and third-order effects, balance technical, security, regulatory, operational, and investment trade-offs, and connect engineering decisions to Compeer's strategy, client needs, enterprise value, and durable multi-year direction. Exceptional communication, influence, leadership, and learning agility, including the ability to advise executives and governance bodies, constructively challenge assumptions, resolve cross-organizational dependencies, mentor senior technical talent, and rapidly evaluate and responsibly adopt emerging technologies. How we will take care of you: Our job titles may span more than one career level (associate, senior, principal, etc.). The actual title and base pay offered is dependent upon many factors, such as: training, transferable skills, work experience, business needs and market demands. The base pay range is subject to change and may be modified in the future. This role is eligible for variable compensation and other benefits. Base Pay $133,000-$180,000 USD Compeer Financial is an equal opportunity employer and all qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, disability status, protected veteran status, or any other characteristic protected by law. Must be authorized to work for any employer in the United States. Compeer is unable to sponsor or take over sponsorship of an employment visa at this time. Click here to view federal employment laws applicable for applicants.

Job Description Job Description Empowered to live. Inspired to work. Compeer Financial is a member-owned cooperative located in Illinois, Minnesota and Wisconsin. We bring together team members with a variety of backgrounds and experiences to help provide financial services to support agriculture and rural communities. Join us in a culture that not only promotes meaningful work and professional development, but provides a flexible, hybrid work environment and excellent benefits, which empower you to thrive both personally and professionally. How we support you: Hybrid model - up to 50% work from home Flexible schedules including ample flexibility in the summer months Up to 9% towards 401k (3% fixed Compeer contribution plus up to 6% match) Benefits: medical, dental, vision, HSA/FSA, life & AD&D insurance, short-term and long-term disability, wellness program & EAP Vacation, sick leave, holidays/floating holidays, parental leave, and volunteer paid time off Learning and development programs Mentorship programs Cross-functional committee opportunities (i.e. Inclusion Council, emerging professional groups, etc.) Professional membership/certification reimbursement and more! Casual/seasonal & intern team members are not eligible for benefits except for state-mandated programs. To learn more about Compeer Financial visit Where you will work: This position offers a hybrid work option up to 50% and is open to any Compeer office location in Illinois, Minnesota and Wisconsin. The contributions you will make: The highest-level individual contributor in the software engineering career framework, providing enterprise technical leadership across portfolios, products, platforms, and transformation initiatives, with a focus on leading and expanding responsible AI use across the organization. Sets enterprise AI Enablement Platform direction and partnering with Enterprise Architecture and AI Risk governance workgroup. Connects AI technology platform strategy to enterprise needs, risks, and measurable enterprise value. Advances AI-enabled engineering and Agentic AI at scale, building tools and platforms that make AI adoption safe, repeatable, and accessible. Serves as a trusted advisor to senior technology and business leaders and models the behaviors that strengthen Compeer's engineering culture. A typical day: Leads AI platform engineering and adoption by building enterprise AI platforms, tools, services, and integrations for LLMs, retrieval-augmented generation, vector search, agents, Model Context Protocol, and AI-assisted and agentic software development. Establishes AI operational excellence through reusable components and guardrails, evaluation frameworks, monitoring, cost and performance management, output validation, production support, and responsible scaling of AI tools and agents. Partners on AI governance and risk with Risk Management, Information Security, Legal, Compliance, and Data Governance, translating requirements into access controls, data protection, model and vendor risk management, human oversight, auditability, and use-case risk tiering. Owns AI-enabled and Agentic AI engineering delivery for the most strategic and ambiguous software initiatives from discovery through sustained operation, making significant cross-boundary technical decisions and remaining accountable for quality, security, reliability, scalability, and production performance. Develops engineers and technical leaders through mentoring, communities of practice, training, and reusable guidance on AI-assisted development, prompt and context engineering, responsible AI, agentic development, and critical evaluation of AI-generated output. Advances technical mastery and engineering excellence across architecture, distributed systems, integration, cloud-native engineering, DevSecOps, testing, CI/CD, security, observability, and reliability while evaluating emerging technologies through experimentation and evidence. Connects engineering to business outcomes by translating Compeer's strategy, client needs, regulatory obligations, and risk into engineering strategies, investment options, build-versus-buy decisions, high-value AI opportunities, and multi-year roadmaps. Leads across organizational boundaries by partnering with engineering, architecture, product, security, data, operations, quality, risk, and business leaders Influences without authority, resolving conflicts and dependencies, and communicating risks and trade-offs clearly. The skills and experience we prefer you have: Bachelor's degree in Computer Science, Software Engineering, or related discipline, or equivalent experience, with extensive experience designing, building, and operating complex, business-critical software, distributed systems, and platforms. Hands-on experience designing and building AI applications and platforms using LLMs, retrieval-augmented generation, vector search, agent orchestration, Model Context Protocol, and cloud AI services such as Azure OpenAI or Azure AI Foundry. Experience scaling responsible AI-enabled engineering and Agentic AI across the software lifecycle, prompt and model versioning, quality and safety testing, cost and performance monitoring, production support, and validation of AI-generated outcomes. Working knowledge of responsible AI and AI governance, model and vendor risk management, privacy, bias and fairness, auditability, human oversight, sensitive-data protection, and translating governance requirements into practical technical controls; experience in financial services, agriculture, or another regulated industry is preferred. Experience leading complex solution design and strategic technical decisions across multiple portfolios, products, systems, or business domains, including architecture, integration, technology trade-offs, engineering strategy, standards, governance, and target-state architecture. Distinguished software engineering expertise across application development, APIs and integration, cloud-native and event-driven architectures, data, security, platforms, DevSecOps, testing, CI/CD, observability, reliability, and operational excellence. Ability to analyze highly complex problems, anticipate second- and third-order effects, balance technical, security, regulatory, operational, and investment trade-offs, and connect engineering decisions to Compeer's strategy, client needs, enterprise value, and durable multi-year direction. Exceptional communication, influence, leadership, and learning agility, including the ability to advise executives and governance bodies, constructively challenge assumptions, resolve cross-organizational dependencies, mentor senior technical talent, and rapidly evaluate and responsibly adopt emerging technologies. How we will take care of you: Our job titles may span more than one career level (associate, senior, principal, etc.). The actual title and base pay offered is dependent upon many factors, such as: training, transferable skills, work experience, business needs and market demands. The base pay range is subject to change and may be modified in the future. This role is eligible for variable compensation and other benefits. Base Pay $133,000-$180,000 USD Compeer Financial is an equal opportunity employer and all qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, disability status, protected veteran status, or any other characteristic protected by law. Must be authorized to work for any employer in the United States. Compeer is unable to sponsor or take over sponsorship of an employment visa at this time. Click here to view federal employment laws applicable for applicants.

Job Description Job Description Empowered to live. Inspired to work. Compeer Financial is a member-owned cooperative located in Illinois, Minnesota and Wisconsin. We bring together team members with a variety of backgrounds and experiences to help provide financial services to support agriculture and rural communities. Join us in a culture that not only promotes meaningful work and professional development, but provides a flexible, hybrid work environment and excellent benefits, which empower you to thrive both personally and professionally. How we support you: Hybrid model - up to 50% work from home Flexible schedules including ample flexibility in the summer months Up to 9% towards 401k (3% fixed Compeer contribution plus up to 6% match) Benefits: medical, dental, vision, HSA/FSA, life & AD&D insurance, short-term and long-term disability, wellness program & EAP Vacation, sick leave, holidays/floating holidays, parental leave, and volunteer paid time off Learning and development programs Mentorship programs Cross-functional committee opportunities (i.e. Inclusion Council, emerging professional groups, etc.) Professional membership/certification reimbursement and more! Casual/seasonal & intern team members are not eligible for benefits except for state-mandated programs. To learn more about Compeer Financial visit Where you will work: This position offers a hybrid work option up to 50% and is open to any Compeer office location in Illinois, Minnesota and Wisconsin. The contributions you will make: The highest-level individual contributor in the software engineering career framework, providing enterprise technical leadership across portfolios, products, platforms, and transformation initiatives, with a focus on leading and expanding responsible AI use across the organization. Sets enterprise AI Enablement Platform direction and partnering with Enterprise Architecture and AI Risk governance workgroup. Connects AI technology platform strategy to enterprise needs, risks, and measurable enterprise value. Advances AI-enabled engineering and Agentic AI at scale, building tools and platforms that make AI adoption safe, repeatable, and accessible. Serves as a trusted advisor to senior technology and business leaders and models the behaviors that strengthen Compeer's engineering culture. A typical day: Leads AI platform engineering and adoption by building enterprise AI platforms, tools, services, and integrations for LLMs, retrieval-augmented generation, vector search, agents, Model Context Protocol, and AI-assisted and agentic software development. Establishes AI operational excellence through reusable components and guardrails, evaluation frameworks, monitoring, cost and performance management, output validation, production support, and responsible scaling of AI tools and agents. Partners on AI governance and risk with Risk Management, Information Security, Legal, Compliance, and Data Governance, translating requirements into access controls, data protection, model and vendor risk management, human oversight, auditability, and use-case risk tiering. Owns AI-enabled and Agentic AI engineering delivery for the most strategic and ambiguous software initiatives from discovery through sustained operation, making significant cross-boundary technical decisions and remaining accountable for quality, security, reliability, scalability, and production performance. Develops engineers and technical leaders through mentoring, communities of practice, training, and reusable guidance on AI-assisted development, prompt and context engineering, responsible AI, agentic development, and critical evaluation of AI-generated output. Advances technical mastery and engineering excellence across architecture, distributed systems, integration, cloud-native engineering, DevSecOps, testing, CI/CD, security, observability, and reliability while evaluating emerging technologies through experimentation and evidence. Connects engineering to business outcomes by translating Compeer's strategy, client needs, regulatory obligations, and risk into engineering strategies, investment options, build-versus-buy decisions, high-value AI opportunities, and multi-year roadmaps. Leads across organizational boundaries by partnering with engineering, architecture, product, security, data, operations, quality, risk, and business leaders Influences without authority, resolving conflicts and dependencies, and communicating risks and trade-offs clearly. The skills and experience we prefer you have: Bachelor's degree in Computer Science, Software Engineering, or related discipline, or equivalent experience, with extensive experience designing, building, and operating complex, business-critical software, distributed systems, and platforms. Hands-on experience designing and building AI applications and platforms using LLMs, retrieval-augmented generation, vector search, agent orchestration, Model Context Protocol, and cloud AI services such as Azure OpenAI or Azure AI Foundry. Experience scaling responsible AI-enabled engineering and Agentic AI across the software lifecycle, prompt and model versioning, quality and safety testing, cost and performance monitoring, production support, and validation of AI-generated outcomes. Working knowledge of responsible AI and AI governance, model and vendor risk management, privacy, bias and fairness, auditability, human oversight, sensitive-data protection, and translating governance requirements into practical technical controls; experience in financial services, agriculture, or another regulated industry is preferred. Experience leading complex solution design and strategic technical decisions across multiple portfolios, products, systems, or business domains, including architecture, integration, technology trade-offs, engineering strategy, standards, governance, and target-state architecture. Distinguished software engineering expertise across application development, APIs and integration, cloud-native and event-driven architectures, data, security, platforms, DevSecOps, testing, CI/CD, observability, reliability, and operational excellence. Ability to analyze highly complex problems, anticipate second- and third-order effects, balance technical, security, regulatory, operational, and investment trade-offs, and connect engineering decisions to Compeer's strategy, client needs, enterprise value, and durable multi-year direction. Exceptional communication, influence, leadership, and learning agility, including the ability to advise executives and governance bodies, constructively challenge assumptions, resolve cross-organizational dependencies, mentor senior technical talent, and rapidly evaluate and responsibly adopt emerging technologies. How we will take care of you: Our job titles may span more than one career level (associate, senior, principal, etc.). The actual title and base pay offered is dependent upon many factors, such as: training, transferable skills, work experience, business needs and market demands. The base pay range is subject to change and may be modified in the future. This role is eligible for variable compensation and other benefits. Base Pay $133,000-$180,000 USD Compeer Financial is an equal opportunity employer and all qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, disability status, protected veteran status, or any other characteristic protected by law. Must be authorized to work for any employer in the United States. Compeer is unable to sponsor or take over sponsorship of an employment visa at this time. Click here to view federal employment laws applicable for applicants.

Job Description Job Description Empowered to live. Inspired to work. Compeer Financial is a member-owned cooperative located in Illinois, Minnesota and Wisconsin. We bring together team members with a variety of backgrounds and experiences to help provide financial services to support agriculture and rural communities. Join us in a culture that not only promotes meaningful work and professional development, but provides a flexible, hybrid work environment and excellent benefits, which empower you to thrive both personally and professionally. How we support you: Hybrid model - up to 50% work from home Flexible schedules including ample flexibility in the summer months Up to 9% towards 401k (3% fixed Compeer contribution plus up to 6% match) Benefits: medical, dental, vision, HSA/FSA, life & AD&D insurance, short-term and long-term disability, wellness program & EAP Vacation, sick leave, holidays/floating holidays, parental leave, and volunteer paid time off Learning and development programs Mentorship programs Cross-functional committee opportunities (i.e. Inclusion Council, emerging professional groups, etc.) Professional membership/certification reimbursement and more! Casual/seasonal & intern team members are not eligible for benefits except for state-mandated programs. To learn more about Compeer Financial visit Where you will work: This position offers a hybrid work option up to 50% and is open to any Compeer office location in Illinois, Minnesota and Wisconsin. The contributions you will make: The highest-level individual contributor in the software engineering career framework, providing enterprise technical leadership across portfolios, products, platforms, and transformation initiatives, with a focus on leading and expanding responsible AI use across the organization. Sets enterprise AI Enablement Platform direction and partnering with Enterprise Architecture and AI Risk governance workgroup. Connects AI technology platform strategy to enterprise needs, risks, and measurable enterprise value. Advances AI-enabled engineering and Agentic AI at scale, building tools and platforms that make AI adoption safe, repeatable, and accessible. Serves as a trusted advisor to senior technology and business leaders and models the behaviors that strengthen Compeer's engineering culture. A typical day: Leads AI platform engineering and adoption by building enterprise AI platforms, tools, services, and integrations for LLMs, retrieval-augmented generation, vector search, agents, Model Context Protocol, and AI-assisted and agentic software development. Establishes AI operational excellence through reusable components and guardrails, evaluation frameworks, monitoring, cost and performance management, output validation, production support, and responsible scaling of AI tools and agents. Partners on AI governance and risk with Risk Management, Information Security, Legal, Compliance, and Data Governance, translating requirements into access controls, data protection, model and vendor risk management, human oversight, auditability, and use-case risk tiering. Owns AI-enabled and Agentic AI engineering delivery for the most strategic and ambiguous software initiatives from discovery through sustained operation, making significant cross-boundary technical decisions and remaining accountable for quality, security, reliability, scalability, and production performance. Develops engineers and technical leaders through mentoring, communities of practice, training, and reusable guidance on AI-assisted development, prompt and context engineering, responsible AI, agentic development, and critical evaluation of AI-generated output. Advances technical mastery and engineering excellence across architecture, distributed systems, integration, cloud-native engineering, DevSecOps, testing, CI/CD, security, observability, and reliability while evaluating emerging technologies through experimentation and evidence. Connects engineering to business outcomes by translating Compeer's strategy, client needs, regulatory obligations, and risk into engineering strategies, investment options, build-versus-buy decisions, high-value AI opportunities, and multi-year roadmaps. Leads across organizational boundaries by partnering with engineering, architecture, product, security, data, operations, quality, risk, and business leaders Influences without authority, resolving conflicts and dependencies, and communicating risks and trade-offs clearly. The skills and experience we prefer you have: Bachelor's degree in Computer Science, Software Engineering, or related discipline, or equivalent experience, with extensive experience designing, building, and operating complex, business-critical software, distributed systems, and platforms. Hands-on experience designing and building AI applications and platforms using LLMs, retrieval-augmented generation, vector search, agent orchestration, Model Context Protocol, and cloud AI services such as Azure OpenAI or Azure AI Foundry. Experience scaling responsible AI-enabled engineering and Agentic AI across the software lifecycle, prompt and model versioning, quality and safety testing, cost and performance monitoring, production support, and validation of AI-generated outcomes. Working knowledge of responsible AI and AI governance, model and vendor risk management, privacy, bias and fairness, auditability, human oversight, sensitive-data protection, and translating governance requirements into practical technical controls; experience in financial services, agriculture, or another regulated industry is preferred. Experience leading complex solution design and strategic technical decisions across multiple portfolios, products, systems, or business domains, including architecture, integration, technology trade-offs, engineering strategy, standards, governance, and target-state architecture. Distinguished software engineering expertise across application development, APIs and integration, cloud-native and event-driven architectures, data, security, platforms, DevSecOps, testing, CI/CD, observability, reliability, and operational excellence. Ability to analyze highly complex problems, anticipate second- and third-order effects, balance technical, security, regulatory, operational, and investment trade-offs, and connect engineering decisions to Compeer's strategy, client needs, enterprise value, and durable multi-year direction. Exceptional communication, influence, leadership, and learning agility, including the ability to advise executives and governance bodies, constructively challenge assumptions, resolve cross-organizational dependencies, mentor senior technical talent, and rapidly evaluate and responsibly adopt emerging technologies. How we will take care of you: Our job titles may span more than one career level (associate, senior, principal, etc.). The actual title and base pay offered is dependent upon many factors, such as: training, transferable skills, work experience, business needs and market demands. The base pay range is subject to change and may be modified in the future. This role is eligible for variable compensation and other benefits. Base Pay $133,000-$180,000 USD Compeer Financial is an equal opportunity employer and all qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, disability status, protected veteran status, or any other characteristic protected by law. Must be authorized to work for any employer in the United States. Compeer is unable to sponsor or take over sponsorship of an employment visa at this time. Click here to view federal employment laws applicable for applicants.

Job Description Job Description Company Description Vuori is re-defining what athletic apparel looks like: built to move and sweat in but designed with a casual aesthetic to transition into everyday life. We draw inspiration from an active coastal California lifestyle; an integration of fitness, creative expression and life. Our high energy fast paced retail environment is reflected in the clothes we make. We aim to inspire others to take on all aspects of their lives with clarity, enthusiasm and purpose while having a lot of fun along the way. We are proud to be an outlet for opportunity and for personal growth and success. Job Description As Principal, AI Architect and Governance, you will serve as Vuori's senior technical authority for AI platform design, enterprise architecture standards, and responsible AI. You will define how AI systems are designed, built, integrated, and governed across the organization, ensuring they are secure, compliant, scalable, reliable, and aligned with business priorities. This is a senior role for someone who combines deep, hands-on technical expertise with enterprise influence. You will work directly with engineers on architecture and design decisions, translate emerging AI capabilities into pragmatic enterprise patterns, and advise technology, business, security, legal, and data leaders on platform choices and risk. You will raise the quality and consistency of AI development through clear standards, reusable patterns, design reviews, and governance guardrails, without direct people-management responsibility. What you'll get to do: Set direction and raise the bar Shape the evolution of Vuori's enterprise AI architecture, ensuring it keeps pace with the growth of the business and the maturity of the AI ecosystem. Establish reference architectures, engineering standards, and integration patterns that teams across Vuori use when building AI systems. Lead technical evaluations of AI platforms, foundation models, cloud services, and tooling - recommending adoption decisions based on rigorous analysis of performance, cost, security, and fit. Raise the architectural capability of engineers across Vuori through design reviews, pairing, documentation, and reusable patterns others adopt because they are good. Bring clarity to complex, ambiguous problems - including calling out when a proposed AI approach is not the right solution. Make build-versus-buy-versus-partner recommendations that shape the AI platform for years and communicate the rationale clearly to technical and non-technical audiences. Build and maintain AI governance Develop and operationalize Vuori's AI governance framework, covering model risk management, data privacy, bias detection, audit trails, and regulatory compliance. Establish AI engineering standards, security guardrails, testing and evaluation protocols, and responsible AI policies that apply consistently across all teams building AI systems. Create governance structures that give teams a clear path to building and shipping AI responsibly - balancing speed of innovation with enterprise-grade risk management. Partner with Legal, Compliance, Security, and Data teams to ensure AI systems meet applicable regulations, data handling requirements, and ethical standards. Implement ongoing monitoring, incident response, and continuous improvement processes for AI systems in production. Partner across the organization Work closely with senior technology and business leaders to align AI architecture and governance with organizational priorities and investment decisions. Serve as a clear, credible voice on AI architecture and risk in conversations with executive sponsors, engineering leadership, and external partners or auditors. Collaborate with AI product, engineering, and data teams to ensure architectural and governance requirements are understood and built in from the start - not added after the fact. Mentor engineers and technical leads through architectural guidance, design reviews, and the development of shared standards they can apply independently. Measure and evolve Track and report on platform reliability, governance adherence, security posture, and the business impact of AI architecture investments. Continuously assess the AI landscape and incorporate emerging technologies, best practices, and industry standards to keep Vuori's AI ecosystem well-positioned. Use data and operational experience to refine standards, improve governance processes, and remove friction for teams building AI responsibly. Qualifications Who you are: Bachelor's degree in Computer Science, Engineering, Information Systems, or a related technical field; advanced degree preferred. 12+ years of experience in software engineering, platform engineering, or technology architecture, including significant experience owning enterprise AI or data platform architecture at scale. Demonstrated experience designing and governing AI systems in production - including LLMs, agentic systems, ML pipelines, and model serving infrastructure. Deep expertise in enterprise AI governance, risk management, security, and compliance frameworks. Proven ability to define technical standards, make architecture decisions, and drive adoption across large, cross-functional engineering organizations through influence rather than direct authority. Experience with cloud-native architectures on Azure, AWS, or Google Cloud, and modern AI infrastructure patterns including RAG, vector databases, and orchestration frameworks. Experience building and deploying with frontier AI lab models, platforms, and/or frameworks (i.e. Anthropic, OpenAI, Google, Meta, etc.). Exceptional communication skills, with the ability to explain complex AI architecture and governance requirements clearly to engineering, business, and executive audiences. Strong executive presence and a demonstrated ability to build alignment, challenge assumptions constructively, and influence decisions across a matrixed organization. Preferred Qualifications Experience with enterprise AI governance platforms, model risk management frameworks, or AI regulatory standards such as the EU AI Act or NIST AI RMF. Familiarity with MLOps, model lifecycle management, AI observability, and responsible AI tooling. Experience with low-code and pro-code enterprise AI platforms such as Azure AI Foundry, Microsoft Copilot Studio, or Google Gemini Enterprise. Background in retail, direct-to-consumer, e-commerce, or a similar consumer operating environment. Demonstrated ability to establish and scale practical architecture standards and governance capabilities in high-growth organizations. Active engagement with the broader AI and technology community through publications, speaking, or open-source contributions. Additional Information Our investment in you: At Vuori, we're proud to offer the following to our employees: Health Insurance Savings and Retirement Plan Employee Assistance Program Generous Vuori Discount & Industry Perks Paid Time Off Wellness & Fitness benefits The salary range for this role is $197,500 per year - $264,390 per year. This role is bonus eligible. Vuori is proud to be an Equal Opportunity/Affirmative Action employer. All qualified applicants will receive consideration for employment without regard to sex, gender identity, sexual orientation, race, color, religion, national origin, disability, protected Veteran status, age, or any other characteristic protected by law. All your information will be kept confidential according to EEO guidelines.

Job Description Job Description Genesis10 is currently seeking a Innovation Principal Engineer for a Contract-to-Hire opportunity. This position can work hybrid or can work remotely in the footprint locations of Columbus, OH; Minnetonka, MN; Atlanta, GA: Dallas, TX; Detroit, MI; Chicago, IL; Charlotte, NC; Birmingham, AL; Tupelo, MS; Pittsburgh, PA; Cleveland, OH; Akron, OH and Cincinnati, OH. Compensation: $100.00 - $110.00 per hour, W2, based on qualifications Position Overview: This position will be responsible for driving this organizations most complex innovation initiatives by designing and building end-to-end, production-grade prototypes and reusable technology assets, including AI-enabled and agentic applications within Google Cloud, while setting high engineering standards for solution architecture, application design, security, governance, and delivery acceleration across the Technology Innovation portfolio. This role requires strong hands-on full-stack engineering skills, the mindset of a startup innovator in a large organization, deep experience building AI-integrated applications end-to-end, developing APIs and user experiences, and the ability to operate independently with limited supervision in a fast-paced innovation environment. The ideal candidate will bring demonstrated production experience with Vertex AI or Comparable: Vertex AI strongly preferred. Comparable experience with AWS Bedrock, Microsoft Azure AI Foundry, or Palantir AIP, Gemini, agent frameworks, AI gateway/model-proxy patterns, and enterprise-grade deployment of AI workloads. Key Responsibilities: Lead the design and delivery of high-impact innovation solutions aligned with the organization's business goals and technology trends in financial services Architect and build end-to-end prototypes, production-ready solutions, and reusable assets, including AI-enabled applications, agentic workflows, APIs, workflow automations, and integrations Translate AI innovation concepts, including GenAI, RAG, and agentic patterns, into production-grade applications using Google Cloud technologies such as Vertex AI or Comparable: Vertex AI strongly preferred. Comparable experience with AWS Bedrock, Microsoft Azure AI Foundry, or Palantir AIP and Gemini Architect and implement agent-based and multi-agent systems using frameworks such as Google ADK, Agent Engine, LangGraph, or equivalent technologies Design and oversee cloud-native deployment patterns for AI applications and agent services, ideally using Cloud Run, CI/CD, observability, logging, monitoring, and secure environment configuration Architect or integrate with AI gateways and model-proxy layers supporting model routing, authentication, access controls, guardrails, policy enforcement, and usage governance Define grounding and Retrieval-Augmented Generation (RAG) patterns using Vertex AI Search / Discovery Engine or comparable enterprise retrieval technologies Establish AI governance patterns covering token usage, model consumption, cost management, security controls, guardrails, evaluation, and observability Build solutions with a future-proof mindset, laying a solid foundation for development and preventing the need for costly redesigns Establish and evolve engineering standards for innovation delivery, including reference architectures, reusable templates, code quality, API patterns, agent orchestration patterns, integration patterns, and secure AI development standards Partner with other technology teams to align on enterprise architecture, identity / access patterns, data access, compliance needs, private networking, and reuse opportunities Evaluate emerging technologies and developer tooling, including Digital Assets, AI-assisted development, orchestration frameworks, app frameworks, integration approaches, and AI governance technologies, and assess applicability to the client's environment Drive technical problem-solving across ambiguous domains; proactively decompose work into deliverable increments and lead execution to complete finished outcomes Provide technical mentorship and guidance to engineers, helping improve development velocity, quality, architectural consistency, and confidence across the team Contribute to innovation reporting by summarizing progress, decisions, tradeoffs, and outcomes in a clear way for senior stakeholders Champion a culture of innovation and forward thinking, educating teams about new possibilities and bring an "art of the possible" mindset to problem-solving Primary Requirements: Bachelor's degree in Computer Science, Information Technology, Artificial Intelligence, Machine Learning, or a related technical field Demonstrated hands-on experience building applications end-to-end (backend + integration + UI), including APIs and workflow automation solutions Deep hands-on Google Cloud engineering experience with demonstrated production deployment of applications and AI workloads within GCP Demonstrated production experience building AI-enabled applications using Vertex AI or Comparable: Vertex AI strongly preferred. Comparable experience with AWS Bedrock, Microsoft Azure AI Foundry, or Palantir AIP and Gemini; experience should extend beyond using Gemini or other GenAI tools solely as coding assistants Demonstrated hands-on experience architecting and building agentic or multi-agent systems using Google ADK, Agent Engine, LangGraph, or comparable orchestration frameworks Demonstrated experience building AI-enabled applications involving GenAI, RAG, model integration, evaluation, guardrails, and production deployment Strong experience designing AI gateway, LLM gateway, or model-proxy patterns covering model routing, authentication, security, policy enforcement, and governance Expert-level software engineering and application architecture skills with the ability to independently lead complex initiatives from concept to execution Comfortable operating in a fast-paced, ambiguous environment while managing multiple priorities and driving workplans proactively 10+ years of experience in software engineering, technology architecture, and end-to-end innovative solution delivery Desired Qualifications: Experience building innovative digital products in a regulated environment (financial services preferred) with strong security and risk-awareness Experience deploying AI solutions in highly controlled environments using technologies or patterns such as VPC Service Controls (VPC-SC), private networking, DLP, and secure service-to-service communication Strong API design skills, integration patterns, and experience building services that can be reused across multiple use cases Experience deploying agentic and AI-enabled applications using Google Cloud Run or comparable cloud-native container platforms Experience implementing grounding/RAG solutions using Vertex AI or Comparable: Vertex AI strongly preferred. Comparable experience with AWS Bedrock, Microsoft Azure AI Foundry, or Palantir AIP Search / Discovery Engine or comparable enterprise retrieval technologies Experience implementing AI safety and security controls using Model Armor or comparable technologies Experience managing token usage, model consumption, observability, and AI-related cost governance Experience with modern front-end frameworks and rapid prototyping approaches (e.g., React/Next.js or equivalent), plus strong backend development depth Strong hands-on software engineering experience using one or more modern programming languages (e.g., Java, Python, TypeScript/JavaScript, or similar), with the ability to rapidly learn and apply new technologies as needed Demonstrated success creating reusable accelerators, including templates, libraries, reference implementations, AI patterns, and governance frameworks that scale delivery across teams Experience with AWS Bedrock, Azure OpenAI, or comparable AI platforms is beneficial when combined with demonstrated hands-on Google Cloud AI deployment experience Strong communication skills with the ability to explain architecture decisions, tradeoffs, and outcomes to technical and non-technical audiences Experience building with a product-centric mindset and ability to iteratively delivery; ability to create momentum while maintaining engineering discipline Only candidates available and ready to work directly as Genesis10 employees will be considered for this position. If you have the described qualifications and are interested in this exciting opportunity, please apply! Ranked a Top Staffing Firm in the U.S. by Staffing Industry Analysts for six consecutive years, Genesis10 puts thousands of consultants and employees to work across the United States every year in contract, contract-for-hire, and permanent placement roles. With more than 300 active clients, Genesis10 provides access to many of the Fortune 100 firms and a variety of mid-market organizations across the full spectrum of industry verticals. For contract roles, Genesis10 offers the benefits listed below. If this is a perm-placement opportunity, our recruiter can talk you through the unique benefits offered for that particular client. Benefits of Working with Genesis10: Access to hundreds of clients, most who have been working with Genesis10 for 5-20+ years The opportunity to have a career-home in Genesis10; many of our consultants have been working exclusively with Genesis10 for years Access to an experienced . click apply for full job details

Job Description Job Description Tekfortune is a fast-growing consulting firm specialized in permanent, contract & project-based staffing services for world s leading organizations in a broad range of industries. In this quickly changing economic landscape, virtual recruiting and remote work are critical for the future of work. To support the active project demands and skills gaps, our staffing experts can help you find the best job for you. Position: Agentic QA Engineer Generative AI & Agentic Systems (Agent, Multi-Agent Testing) Location: Dallas, TX _ Onsite only Duration: Long term Mode of interview: One Virtual and One face to face Mode of Job: 5 Days onsite Summary We are seeking a hands-on AI Engineer to design and execute end-to-end testing strategies for agentic AI solutions, including multi-agent systems in production-grade environments. This role partners with the Agentic Operations Team to ensure resiliency, reliability, accuracy, latency, orchestration correctness, and scale. You will establish QA frameworks, build reusable test artifacts, drive macro-level validations across complex workflows, and lead the QA function for Agentic AI from Dev to Prod. Key Responsibilities Agentic & Multi-Agent Testing Reliability, Resiliency, and Latency Accuracy & Macro-Level Validations Scale & Orchestration Dev Prod Readiness Define and own the QA strategy for agentic/multi-agent AI systems across dev, staging, and prod. Mentor a team of QA engineers; establish testing standards, coding guidelines for test harnesses, and review practices. Partner with Agentic Operations, Data Science, MLOps, and Platform teams to embed QA in the SDLC and incident response. Design tests for agent orchestration, tool calling, planner-executor loops, and inter-agent coordination (e.g., task decomposition, handoff integrity, and convergence to goals). Validate state management, context windows, memory/knowledge stores, and prompt/graph correctness under varying conditions. Implement scenario fuzzing (e.g., adversarial inputs, prompt perturbations, tool latency spikes, degraded APIs). Create resilience testing suites: chaos experiments, failover, retries/backoff, circuit-breaking, and degraded mode behavior. Establish latency SLOs and measure end-to-end response times across orchestration layers (LLM calls, tool invocations, queues). Ensure reliability through soak tests, canary verifications, and automated rollbacks. Define ground-truth and reference pipelines for task accuracy (exact match, semantic similarity, factuality checks). Build macro validation frameworks that validate task outcomes across multi-step agent workflows (e.g., complex data pipelines, content generation verification agent loops). Instrument guardrail validations (toxicity, PII, hallucination, policy compliance). Design load/stress tests for multi-agent graphs under scale (concurrency, throughput, queue depth, backpressure). Validate orchestrator correctness (DAG execution, retries, branching, timeouts, compensation paths). Engineer reusable test artifacts (scenario configs, synthetic datasets, prompt libraries, agent graph fixtures, simulators). Integrate tests into CI/CD (pre-merge gates, nightly, canary) and production monitoring with alerting tied to KPIs. Define release criteria and run operational readiness (performance, security, compliance, cost/latency budgets). Required Qualifications 7 years in Software QA/Testing, with 2 years in AI/ML or LLM-based systems; hands-on experience testing agentic/multi-agent architectures. Strong programming skills in Python experience building test harnesses, simulators, and fixtures. Experience with LLM evaluation (exact/soft match, BLEU/ROUGE, BERTScore, semantic similarity via embeddings), guardrails, and prompt testing. Expertise in distributed systems testing latency profiling, resiliency patterns (circuit breakers, retries), chaos engineering, and message queues. Familiarity with orchestration frameworks (LangChain, LangGraph, LlamaIndex, DSPy, OpenAI Assistants/Actions, Azure OpenAI orchestration, or similar). Proficiency with CI/CD (GitHub Actions/Azure DevOps), observability (OpenTelemetry, Prometheus/Grafana, Datadog), and feature flags/canaries. Solid understanding of privacy/security/compliance in AI systems (PII handling, content policies, model safety). Excellent communication and leadership skills; proven ability to work cross-functionally with Ops, Data, and Engineering. Preferred Qualifications Experience with multi-agent simulators, agent graph testing, and tooling latency emulation. Knowledge of MLOps (model versioning, datasets, evaluation pipelines) and A/B experimentation for LLMs. Background in cloud (AWS), serverless, containerization, and event-driven architectures. Prior ownership of cost/latency/SLAs for AI workloads in production. For more information and other jobs available please contact our recruitment team at . To view all the jobs available in the USA and Asia please visit our website at

AI Engineer 5 AI Foundations, LLM Core and Agentic AI) Overview: At Capital One, we are creating responsible and reliable AI systems, changing banking for good. For years, Capital One has been an industry leader in using machine learning to create real-time, personalized customer experiences. Our investments in technology infrastructure and world-class talent - along with our deep experience in machine learning - position us to be at the forefront of enterprises leveraging AI. From informing customers about unusual charges to answering their questions in real time, our applications of AI & ML are bringing humanity and simplicity to banking. We are committed to continuing to build world-class applied science and engineering teams to deliver our industry leading capabilities with breakthrough product experiences and scalable, high-performance AI infrastructure. At Capital One, you will help bring the transformative power of emerging AI capabilities to reimagine how we serve our customers and businesses who have come to love the products and services we build. Team Description: The Intelligent Foundations and Experiences (IFX) team is at the center of bringing our vision for AI at Capital One to life. We work hand-in-hand with our partners across the company to advance the state of the art in science and AI engineering, and we build and deploy proprietary solutions that are central to our business and deliver value to millions of customers. Our AI models and platforms empower teams across Capital One to enhance their products with the transformative power of AI, in responsible and scalable ways for the highest leverage impact. What You'll Do: Partner with a cross-functional team of engineers, research scientists, technical program managers, and product managers to deliver AI-powered products that change how our associates work and how our customers interact with Capital One. Design, develop, test, deploy, and support AI software components including foundation model training, large language model inference, agents and multi-agent workflows, similarity search, guardrails, model evaluation, experimentation, governance, and observability, etc. Leverage a broad stack of Open Source and SaaS AI technologies such as AWS Ultraclusters, Huggingface, VectorDBs, PyTorch, and more. Invent and introduce state-of-the-art foundation model optimization techniques to improve the performance - scalability, cost, latency, throughput - of large scale production AI systems. Contribute to the technical vision and the long term roadmap of foundational AI systems at Capital One. Design, implement and optimize multi-model orchestration pipelines - integrating LLMs, vector search, and domain-specific models into unified systems Establish and lead cost-performance governance reviews across AI systems, tracking GPU utilization, model throughput, and inference cost efficiency Lead team design councils or design review boards to ensure technical consistency and compliance with AI engineering standards Mentor Principal and Manager-level AI engineers, fostering cross-domain learning and elevating organizational technical maturity The Ideal Candidate: You love to build systems, take pride in the quality of your work, and also share our passion to do the right thing. You want to work on problems that will help change banking for good Passion for staying abreast of the latest research, and an ability to intuitively understand scientific publications and judiciously apply novel techniques in production You adapt quickly and thrive on bringing clarity to big, undefined problems. You love asking questions and digging deep to uncover the root of problems and can articulate your findings concisely with clarity. You have the courage to share new ideas even when they are unproven You are deeply Technical. You possess a strong foundation in engineering and mathematics, and your expertise in hardware, software, and AI enables you to see and exploit optimization opportunities that others miss You are a resilient trailblazer who can forge new paths to achieve business goals when the route is unknown Basic Qualifications: Bachelor's degree in Computer Science, AI, Electrical Engineering, Computer Engineering, or related fields plus at least 6 years of experience developing AI and ML algorithms or technologies, or a Master's degree in Computer Science, AI, Electrical Engineering, Computer Engineering, or related fields plus at least 4 years of experience developing AI and ML algorithms or technologies At least 6 years of experience programming with Python, Go, Scala, CUDA, or Java Preferred Qualifications: Experience leading development AI systems with tradeoff decisions around cost, latency, throughput and accuracy 7 years of experience deploying scalable and responsible AI solutions on cloud platforms (e.g. AWS, Google Cloud, Azure, or equivalent private cloud) Experience designing, developing, delivering, and supporting complex AI systems Experience developing AI and ML algorithms or technologies (e.g. LLM Inference, Similarity Search and VectorDBs, Guardrails, Memory) using Python, C++, C#, Java, CUDA, or Golang Experience developing and applying state-of-the-art techniques for optimizing training and inference software to improve hardware utilization, latency, throughput, and cost Experience in building agentic AI systems and agentic workflows Passion for staying abreast of the latest AI research and AI systems, and judiciously apply novel techniques in production Excellent communication and presentation skills, with the ability to articulate complex AI concepts to peers Experience architecting and integrating heterogeneous AI systems - including rule-based, retrieval-augmented, and generative components - into unified production pipelines Experience defining and enforcing standards for ethical AI deployment, including explainability, fairness, and human-in-the-loop review processes Demonstrated ability to balance model performance and operational cost through dynamic inference strategies and model compression Experience right-sizing models, instance counts, and hardware types given requirements (e.g., context length, token inputs, token outputs) Capital One will consider sponsoring a new qualified applicant for employment authorization for this position. The minimum and maximum full-time annual salaries for this role are listed below, by location. Please note that this salary information is solely for candidates hired to perform work within one of these locations, and refers to the amount Capital One is willing to pay at the time of this posting. Salaries for part-time roles will be prorated based upon the agreed upon number of hours to be regularly worked. Cambridge, MA: $229,900 - $262,400 for AI Engineer 5 McLean, VA: $229,900 - $262,400 for AI Engineer 5 New York, NY: $250,800 - $286,200 for AI Engineer 5 San Francisco, CA: $250,800 - $286,200 for AI Engineer 5 San Jose, CA: $250,800 - $286,200 for AI Engineer 5 Candidates hired to work in other locations will be subject to the pay range associated with that location, and the actual annualized salary amount offered to any candidate at the time of hire will be reflected solely in the candidate's offer letter. This role is also eligible to earn performance based incentive compensation, which may include cash bonus(es) and/or long term incentives (LTI). Incentives could be discretionary or non discretionary depending on the plan. Capital One offers a comprehensive, competitive, and inclusive set of health, financial and other benefits that support your total well-being. Learn more at the Capital One Careers website . Eligibility varies based on full or part-time status, exempt or non-exempt status, and management level. This role is expected to accept applications for a minimum of 5 business days.No agencies please. Capital One is an equal opportunity employer (EOE, including disability/vet) committed to non-discrimination in compliance with applicable federal, state, and local laws. Capital One promotes a drug-free workplace. Capital One will consider for employment qualified applicants with a criminal history in a manner consistent with the requirements of applicable laws regarding criminal background inquiries, including, to the extent applicable, Article 23-A of the New York Correction Law; San Francisco, California Police Code Article 49, Sections ; New York City's Fair Chance Act; Philadelphia's Fair Criminal Records Screening Act; and other applicable federal, state, and local laws and regulations regarding criminal background inquiries. If you have visited our website in search of information on employment opportunities or to apply for a position, and you require an accommodation, please contact Capital One Recruiting at 1- or via email at . All information you provide will be kept confidential and will be used only to the extent required to provide needed reasonable accommodations. For technical support or questions about Capital One's recruiting process, please send an email to Capital One does not provide, endorse nor guarantee and is not liable for third-party products, services, educational tools or other information available through this site. Capital One Financial is made up of several different entities. Please note that any position posted in Canada is for Capital One Canada . click apply for full job details

AI Engineer 5 (GenAI Platform, Agentic Infrastructure) At Capital One, we are creating responsible and reliable AI systems, changing banking for good. For years, Capital One has been an industry leader in using machine learning to create real-time, personalized customer experiences. Our investments in technology infrastructure and world-class talent - along with our deep experience in machine learning - position us to be at the forefront of enterprises leveraging AI. From informing customers about unusual charges to answering their questions in real time, our applications of AI & ML are bringing humanity and simplicity to banking. We are committed to continuing to build world-class applied science and engineering teams to deliver our industry leading capabilities with breakthrough product experiences and scalable, high-performance AI infrastructure. At Capital One, you will help bring the transformative power of emerging AI capabilities to reimagine how we serve our customers and businesses who have come to love the products and services we build. Team Description: The Intelligent Foundations and Experiences (IFX) team is at the center of bringing our vision for AI at Capital One to life. We work hand-in-hand with our partners across the company to advance the state of the art in science and AI engineering, and we build and deploy proprietary solutions that are central to our business and deliver value to millions of customers. Our AI models and platforms empower teams across Capital One to enhance their products with the transformative power of AI, in responsible and scalable ways for the highest leverage impact. What You'll Do: Partner with a cross-functional team of engineers, research scientists, technical program managers, and product managers to deliver AI-powered products that change how our associates work and how our customers interact with Capital One. Design, develop, test, deploy, and support AI software components including foundation model training, large language model inference, agents and multi-agent workflows, similarity search, guardrails, model evaluation, experimentation, governance, and observability, etc. Leverage a broad stack of Open Source and SaaS AI technologies such as AWS Ultraclusters, Huggingface, VectorDBs, PyTorch, and more. Invent and introduce state-of-the-art foundation model optimization techniques to improve the performance - scalability, cost, latency, throughput - of large scale production AI systems. Contribute to the technical vision and the long term roadmap of foundational AI systems at Capital One. Design, implement and optimize multi-model orchestration pipelines - integrating LLMs, vector search, and domain-specific models into unified systems Establish and lead cost-performance governance reviews across AI systems, tracking GPU utilization, model throughput, and inference cost efficiency Lead team design councils or design review boards to ensure technical consistency and compliance with AI engineering standards Mentor Principal and Manager-level AI engineers, fostering cross-domain learning and elevating organizational technical maturity The Ideal Candidate: You love to build systems, take pride in the quality of your work, and also share our passion to do the right thing. You want to work on problems that will help change banking for good Passion for staying abreast of the latest research, and an ability to intuitively understand scientific publications and judiciously apply novel techniques in production You adapt quickly and thrive on bringing clarity to big, undefined problems. You love asking questions and digging deep to uncover the root of problems and can articulate your findings concisely with clarity. You have the courage to share new ideas even when they are unproven You are deeply Technical. You possess a strong foundation in engineering and mathematics, and your expertise in hardware, software, and AI enables you to see and exploit optimization opportunities that others miss You are a resilient trailblazer who can forge new paths to achieve business goals when the route is unknown Basic Qualifications: Bachelor's degree in Computer Science, AI, Electrical Engineering, Computer Engineering, or related fields plus at least 6 years of experience developing AI and ML algorithms or technologies, or a Master's degree in Computer Science, AI, Electrical Engineering, Computer Engineering, or related fields plus at least 4 years of experience developing AI and ML algorithms or technologies At least 6 years of experience programming with Python, Go, Scala, CUDA, or Java Preferred Qualifications: Experience leading development AI systems with tradeoff decisions around cost, latency, throughput and accuracy 7 years of experience deploying scalable and responsible AI solutions on cloud platforms (e.g. AWS, Google Cloud, Azure, or equivalent private cloud) Experience designing, developing, delivering, and supporting complex AI systems Experience developing AI and ML algorithms or technologies (e.g. LLM Inference, Similarity Search and VectorDBs, Guardrails, Memory) using Python, C++, C#, Java, CUDA, or Golang Experience developing and applying state-of-the-art techniques for optimizing training and inference software to improve hardware utilization, latency, throughput, and cost Experience in building agentic AI systems and agentic workflows Passion for staying abreast of the latest AI research and AI systems, and judiciously apply novel techniques in production Excellent communication and presentation skills, with the ability to articulate complex AI concepts to peers Experience architecting and integrating heterogeneous AI systems - including rule-based, retrieval-augmented, and generative components - into unified production pipelines Experience defining and enforcing standards for ethical AI deployment, including explainability, fairness, and human-in-the-loop review processes Demonstrated ability to balance model performance and operational cost through dynamic inference strategies and model compression Experience right-sizing models, instance counts, and hardware types given requirements (e.g., context length, token inputs, token outputs) Capital One will consider sponsoring a new qualified applicant for employment authorization for this position. The minimum and maximum full-time annual salaries for this role are listed below, by location. Please note that this salary information is solely for candidates hired to perform work within one of these locations, and refers to the amount Capital One is willing to pay at the time of this posting. Salaries for part-time roles will be prorated based upon the agreed upon number of hours to be regularly worked. Cambridge, MA: $229,900 - $262,400 for AI Engineer 5 McLean, VA: $229,900 - $262,400 for AI Engineer 5 New York, NY: $250,800 - $286,200 for AI Engineer 5 San Francisco, CA: $250,800 - $286,200 for AI Engineer 5 San Jose, CA: $250,800 - $286,200 for AI Engineer 5 Candidates hired to work in other locations will be subject to the pay range associated with that location, and the actual annualized salary amount offered to any candidate at the time of hire will be reflected solely in the candidate's offer letter. This role is also eligible to earn performance based incentive compensation, which may include cash bonus(es) and/or long term incentives (LTI). Incentives could be discretionary or non discretionary depending on the plan. Capital One offers a comprehensive, competitive, and inclusive set of health, financial and other benefits that support your total well-being. Learn more at the Capital One Careers website . Eligibility varies based on full or part-time status, exempt or non-exempt status, and management level. This role is expected to accept applications for a minimum of 5 business days.No agencies please. Capital One is an equal opportunity employer (EOE, including disability/vet) committed to non-discrimination in compliance with applicable federal, state, and local laws. Capital One promotes a drug-free workplace. Capital One will consider for employment qualified applicants with a criminal history in a manner consistent with the requirements of applicable laws regarding criminal background inquiries, including, to the extent applicable, Article 23-A of the New York Correction Law; San Francisco, California Police Code Article 49, Sections ; New York City's Fair Chance Act; Philadelphia's Fair Criminal Records Screening Act; and other applicable federal, state, and local laws and regulations regarding criminal background inquiries. If you have visited our website in search of information on employment opportunities or to apply for a position, and you require an accommodation, please contact Capital One Recruiting at 1- or via email at . All information you provide will be kept confidential and will be used only to the extent required to provide needed reasonable accommodations. For technical support or questions about Capital One's recruiting process, please send an email to Capital One does not provide, endorse nor guarantee and is not liable for third-party products, services, educational tools or other information available through this site. Capital One Financial is made up of several different entities. Please note that any position posted in Canada is for Capital One Canada . click apply for full job details