311 jobs found

Job Description AI Optimization Engineer Location: Charlotte, NC (Hybrid, 3 days on-site at Optimist Hall Location) or Remote within the U.S. (must be able to work EST hours) Compensation: $80 - $95/hour plus benefits Duration: 12-month contract with strong potential for extension or conversion to full-time employment Employment Requirements: W-2 only. No Corp-to-Corp or 1099 arrangements. Successful candidate must be able to work on W-2 without visa sponsorship both now and in the future. ABOUT THE ROLE Our Client is seeking an AI Optimization Engineer to develop and support the optimization engine at the core of their Capital Investment Planning & Prioritization Tool (CIPT). This tool empowers planning teams to make data-driven decisions about which capital investments to fund, when, and why, by transforming complex business rules into robust, explainable, and auditable optimization models. The role involves designing, building, testing, and maintaining advanced mathematical optimization models-primarily using Mixed-Integer Linear Programming (MILP)-and integrating predictive analytics and benefit measures into portfolio planning. You will work closely with data and ML engineers, leverage cloud-based solutions (primarily AWS), and utilize modern software development practices, including AI-enabled tools like GitHub Copilot and Spec-Driven Development. The ideal candidate combines strong optimization and software engineering fundamentals with a willingness to learn new AI-driven approaches, ensuring solutions are mathematically correct, reproducible, and transparent for planners, finance partners, and regulators. WHAT YOU'LL DO Translate complex capital planning business rules-including budget, resource, cash-flow, jurisdictional, and portfolio constraints-into mathematical optimization models. Formulate, implement, and tune Mixed-Integer Linear Programming (MILP) models using Python frameworks (e.g., Pyomo, PuLP, OR-Tools) and commercial/open-source solvers (e.g., Gurobi, CPLEX, HiGHS, CBC). Integrate predictive model outputs (e.g., Probability of Failure, Remaining Useful Life) and benefit measures (e.g., reliability, safety, financial, NPV) into optimization objectives for portfolio planning. Design, build, and maintain scenario optimization workflows, including scenario creation, copying, optimization across planning cycles, and deterministic run harnesses for reproducible results. Develop diagnostic and infeasibility analysis capabilities (e.g., constraint relaxation, irreducible infeasible subsets, clear error messages) to help planners understand optimization outcomes. Evaluate and recommend solvers and modeling approaches based on solution quality, run-time performance, licensing cost, and operational fit. Package and deploy optimization workloads as scalable cloud services (e.g., AWS Batch, Fargate, Lambda, Step Functions) with REST APIs, leveraging Infrastructure as Code tools (e.g., Terraform). Collaborate with data and ML engineers to integrate asset-health predictions and ensure seamless data flow into optimization models. Build automated test suites for optimization models, including unit tests for constraints, regression tests, reproducibility checks, and comparisons against historical results. Profile and tune model performance using reformulation, tighter bounds, decomposition, warm starts, and solver parameter tuning to meet run-time and scalability targets. Produce clear, explainable outputs for planners and stakeholders, including binding constraints, shadow prices, scenario comparisons, and investment selection rationale. Document model formulations, assumptions, and limitations for business owners, auditors, and future engineers. Contribute reusable optimization components, model templates, test harnesses, and engineering accelerators for broader team and organizational use. Utilize GitHub, GitHub Copilot, and AI agents to enhance software development, testing, and delivery processes; gain hands-on experience with agentic software engineering and automation workflows. Prepare, validate, and troubleshoot large input datasets and database integrations (e.g., PostgreSQL) for optimization models, ensuring data quality and version control. Participate in technical learning activities, communities of practice, hackathons, and innovation events to continuously expand cloud, AI, and optimization engineering skills. WHAT YOU BRING Associate's degree in Information Technology or related field required; Bachelor's or Master's in Operations Research, Industrial Engineering, Applied Mathematics, Computer Science, or related field preferred. 5+ years of relevant work experience in optimization, software development, or decision-support applications. Hands-on experience designing, building, and supporting optimization models, especially Mixed-Integer Linear Programming (MILP) using frameworks like Pyomo, PuLP, OR-Tools, or Gurobi Python API. Proficiency with commercial or open-source solvers (e.g., Gurobi, CPLEX, HiGHS, CBC) and strong understanding of linear and integer programming, including advanced formulation techniques (e.g., binary selection variables, logical/indicator constraints, big-M, multi-period timing, precedence constraints). Experience modeling knapsack-style and resource-constrained selection or scheduling problems, multi-objective or weighted-objective optimization, and tradeoff analysis. Ability to diagnose and improve optimization models (e.g., infeasibility, unboundedness, performance tuning, solver settings, reformulation, bounds tightening, decomposition). Strong Python programming skills for optimization, data processing, and service development. Experience developing applications or services on AWS or similar cloud platforms, with familiarity in compute-intensive and batch workloads (e.g., AWS Batch, Fargate/ECS, Lambda, Step Functions, S3, RDS, SageMaker). Familiarity with Infrastructure as Code tools (e.g., Terraform) and modern application architectures (APIs, containers, serverless, event-driven, distributed systems). Experience with Git and GitHub for source control and collaborative development, and interest in AI-enabled development tools (e.g., GitHub Copilot, AI agents, Spec-Driven Development). Experience working with relational, NoSQL, or cloud-based databases (PostgreSQL preferred), including data modeling, validation, and troubleshooting application/data-access issues. Understanding of determinism and reproducibility in solver-based systems (e.g., MIP gaps, tolerances, threads, seeds), and familiarity with sensitivity analysis, duals, and shadow prices. Strong communication skills and ability to explain technical concepts and optimization results to business users, planners, and stakeholders. Experience with capital planning, portfolio optimization, asset management, or utility planning domains is a plus. Experience contributing to reusable software components, frameworks, templates, or shared engineering solutions is a plus. Participation in technical learning activities, communities of practice, hackathons, or innovation events is a plus. Willingness to expand cloud engineering and AI-enabled development skills as part of the role. Familiarity with financial evaluation concepts (e.g., present value, NPV, escalation, discounting) and predictive modeling outputs (e.g., probability of failure, remaining useful life, risk scoring) is a plus. Eight Eleven Group provides equal employment opportunities (EEO) to all employees and applicants for employment without regard to race, color, religion, national origin, age, sex, citizenship, disability, genetic information, gender, sexual orientation, gender identity, marital status, amnesty, or status as a covered veteran in accordance with applicable federal, state, and local laws.

Job Description Role Summary We are seeking a senior, hands-on GECX Architect to design and implement secure, scalable customer experience solutions on Google Gemini Enterprise for Customer Experience platform. The ideal candidate will combine strong architecture design and coding expertise with deep Contact Center, Google GECX/CCAI, Conversational AI, Agentic AI, GenAI/LLM, and Google Cloud experience. This role requires an innovative architect who can build PoCs and accelerators, troubleshoot complex integrations, and take automation solutions from architecture through production implementation. The architect will analyze requirements, compare solution options, recommend the best approach, guide the team, and personally resolve critical design, development, integration, and platform issues. Key Responsibilities Architect end-to-end GECX solutions across voice, chat, web, mobile, and assisted-service journeys. Translate contact center needs into architecture, technical designs, integration patterns, and delivery plans. Compare Google-native, CCaaS, open-source, and third-party tools; recommend options based on fit, security, scalability, cost, and risk. Build and deploy Agentic Voice and Chat solutions using CX Agent Studio, Conversational Agents, Dialogflow CX, Vertex AI, Gemini, and GCP. Design and implement multi-turn conversations, prompts, orchestration, tool calls, fulfillment, context handling, grounding, and human-agent handoff. Design integrations with IVR, telephony, CCaaS, CRM, APIs, databases, and enterprise systems, including SIP/SIPREC, gRPC, and WebRTC where required. Implement GCP components such as Cloud Run, Cloud Functions, BigQuery, Cloud Storage, IAM, logging, monitoring, and data protection services. Working knowledge implementing Services, Components through GCP Stack like GCS, DLP Templates, Cloud Functions, Big Query etc. Working experience with collecting and reviewing reporting data/logs from Google Logs Explorer (Stack Driver), Google Big Query, Avaya, GTP, MS SQL. Lead hands-on coding, configuration, PoCs, troubleshooting, CI/CD, deployment, and production stabilization. Define AI evaluation for task success, response quality, grounding, hallucinations, guardrails, tool use, and agent trajectory. Guide teams, review designs and code, remove technical blockers, and ensure solutions reach production. Required Skills & Technology 12+ years in solution architecture and engineering, including enterprise contact center, conversational AI, or AI/ML delivery. Hands-on expertise in GECX / Customer Engagement Suite, Google CCAI, CX Agent Studio / Conversational Agents, Dialogflow CX, Vertex AI, Gemini, and GCP. Experience building Agentic Voice or Agentic Chat solutions using LLMs, orchestration, tool/function calling, grounding, guardrails, and enterprise integrations. Strong contact center knowledge: IVR, CTI, routing, queues, transfers, SIP, ANI/DNIS, DTMF, STT/TTS, and CRM/backend integration. Strong Python and/or Java skills, REST/JSON APIs, SQL, Git, CI/CD, cloud troubleshooting, IAM, and secure delivery. Ability to conduct structured tool assessments, make architecture decisions, lead cross-functional teams, and communicate with client stakeholders. Strong verbal and written communication skills. Ability to collaborate with onshore/offshore resources on functional/technical /process aspects. This is not a governance-only role. The architect must remain hands-on and own critical solutions through production. Preferred Qualifications Google Cloud certification, preferably Professional Cloud Architect or a role-relevant equivalent. Experience delivering large-scale CCaaS, contact center transformation, or production agentic AI programs. Bachelor's degree in Computer Science, Engineering, or a related field, or equivalent practical experience.

Job Description EazyML, () recognized by Gartner, specializes in Responsible AI. Our solutions enable proactive compliance and sustainable automation for enterprises adopting AI at scale. We're also associated with breakthrough startups like Amelia.ai, giving our team exposure to cutting-edge AI products at enterprise scale. About the Role We're looking for a Senior Technical Program Manager who will drive end-to-end program management for AI infrastructure deployments, AI Labs initiatives, AI field deployment programs, automation projects, and AI-driven operational transformation initiatives. The role requires a blend of program management leadership, technical acumen mainly in the AI, change management expertise, executive communication skills, and cross-functional coordination capabilities. Program Leadership & Governance Lead multiple concurrent AI programs and strategic initiatives across Agentic /GenAI, solutions. Define program objectives, scope, schedules, milestones, dependencies, and success criteria. Establish and maintain governance structures, steering committees, executive reviews, and stakeholder communication plans. Develop program roadmaps aligned with customers strategic priorities and growth objectives. Drive Foundation AI projects from kickoff through development to deployment and stabilization Support the customer to translate strategy into executable roadmaps with clear milestones, risks, dependencies, and success metrics Develop project plans consistent with business goals and requirements for algorithm performance Establish and maintain program governance, including planning cadences, status reporting, decision forums, and executive communication Bring together key stakeholders to facilitate timely and lasting decision-making Partner with the extended team on availability constraints, capacity forecasting, deadlines etc Partner with the broader team to continuously improve the development process to enable execution of projects in an organized, methodical, and predictable manner. Identify and manage project risks and issues, ensuring mitigations are in place to reduce potential impacts. Facilitate cross-functional communication across teams for awareness and understanding. Understanding of technical issues to facilitate resolution of project blockers is a plus Proactively identify risks, dependencies, and technical blockers and drive cross-functional resolution with urgency. Lead escalation management and executive communications for high-priority issues. Ensure mitigation strategies are implemented and tracked to closure. QUALIFICATIONS: 10+ years of industry experience in program or project management, preferably involving AI/ML, data platforms or large-scale systems Experience working with AI or applied ML teams Familiarity with agile methodologies, sprint planning, backlog grooming processes Data-driven approach to strategic planning, comfortable reviewing metrics and identifying ways to measure program success Familiarity with AI/ML concepts (e.g., model training, inference, data pipelines, evaluation, experimentation), hands-on coding experience is not required Clear, confident communicator who can bridge deeply technical teams and business stakeholders Proven ability to influence without authority and drive alignment across diverse teams Experience working with research teams Background in consumer technology, platforms, or data-driven products Experience organizing projects using Jira or similar platforms Cross-Functional Leadership Collaborate with: Solutions Architects Engineering Integration Center Operations Delivery Teams Product Management Leadership Facilitate alignment across technical and business stakeholders. Resolve conflicts, remove roadblocks, and drive accountability across programs. Resource & Capacity Planning Coordinate resource demand planning across AI project portfolios. Forecast staffing needs and identify capability gaps. Support workforce planning and bench-strength development initiatives. Contribute to the PMO's strategic vision to improve organizational readiness for future AI growth. AI Enablement & Continuous Improvement Support AI training, certification, and capability development programs. Drive adoption of best practices, standards, automation opportunities, and operational efficiencies. Identify opportunities to leverage AI technologies to improve delivery performance and customer experience. Champion continuous improvement initiatives focused on scalability, quality, and delivery excellence. Behaviors and Competencies Communication: Can effectively communicate complex ideas and information to diverse audiences, facilitate effective communication between others, and mentor others in effective communication. Interpersonal Skills: Can communicate effectively, build relationships, resolve conflicts, influence others, and support others in developing their interpersonal skills in major situations. Follow-Up: Can take ownership of tasks, collaborate with others in managing follow-ups, and drive results through effective task completion. Collaboration: Can take ownership of team initiatives, foster a collaborative environment, and ensure that all team members feel valued and heard. Problem-Solving: Can proactively identify and take ownership of complex problem-solving initiatives, initiate preventative measures, collaborate with others to find solutions, and drive successful outcomes. Time Management: Can manage time effectively, accurately estimate the time required for specific tasks, balance multiple tasks, and help others in improving their time management skills. Organization: Can oversee complex projects with multiple moving parts, ensure team alignment with organizational systems, and adapt to changing priorities. Digital Acumen: Can take ownership of complex digital initiatives, collaborate with others, and drive digital results. Leadership: Can take ownership of complex team initiatives, collaborate with others in decision-making processes, and drive team performance.

Job Description Job Description: Agentic AI Engineer Experience: (8-10) + Years Location: Fort Mill, SC Annual Salary: $120K Per Annum Job Summary We are seeking an experienced Agentic AI Engineer with 10+ years of experience in software engineering, AI/ML, and enterprise application development to design, develop, and deploy intelligent AI agents for wealth management applications. The ideal candidate will have strong expertise in Python, Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), multi-agent architectures, and enterprise AI integration. This role requires hands-on experience building production-grade AI solutions that can reason, plan, use tools, retrieve information, and execute business workflows with appropriate human oversight. Experience in wealth management, financial services, investment management, portfolio analytics, or financial advisory platforms is highly preferred. Key Responsibilities Design, develop, and deploy production-ready Agentic AI solutions for wealth management and financial services applications. Build autonomous and semi-autonomous AI agents capable of planning tasks, reasoning, invoking tools, and executing multi-step workflows. Develop multi-agent systems using frameworks such as LangGraph, LangChain, Semantic Kernel, AutoGen, or equivalent technologies. Design and implement LLM-powered applications using models from OpenAI, Anthropic, Azure OpenAI, AWS Bedrock, or similar platforms. Develop and optimize RAG pipelines, including document ingestion, chunking, embeddings, semantic search, reranking, and context retrieval. Integrate AI agents with enterprise systems, REST APIs, databases, knowledge repositories, and financial data platforms. Implement tool/function calling, structured outputs, workflow orchestration, memory management, and stateful agent execution. Build solutions for wealth management use cases, including portfolio research, advisor assistance, investment document analysis, client servicing, financial insights, and knowledge retrieval. Implement responsible AI practices, including guardrails, prompt-injection protection, access controls, PII protection, auditability, and human-in-the-loop approvals. Evaluate AI solutions for accuracy, groundedness, latency, reliability, cost, and business effectiveness. Deploy, monitor, and maintain AI applications using CI/CD, containerization, cloud services, logging, and observability tools. Collaborate with product managers, architects, data engineers, business analysts, and compliance teams to translate business requirements into scalable AI solutions. Mentor engineers, establish engineering best practices, and provide technical leadership across AI initiatives. Required Technical Skills 1. Programming and Software Engineering Strong hands-on experience with Python and object-oriented programming. Expertise in REST APIs, microservices, asynchronous processing, and enterprise application integration. Proficiency with Git, automated testing, debugging, and software engineering best practices. 2. Agentic AI and LLMs Hands-on experience designing and implementing AI agents and multi-agent workflows. Strong understanding of LLM architecture, prompt engineering, context management, and model selection. Experience with agent orchestration frameworks such as LangGraph, LangChain, Semantic Kernel, or AutoGen. Experience implementing tool calling, function calling, planning, state management, retries, and error handling. Understanding of agent autonomy, workflow determinism, and human-in-the-loop controls. 3. RAG and Knowledge Retrieval Experience building enterprise-grade RAG applications and document intelligence solutions. Knowledge of embedding models, vector search, hybrid search, reranking, and retrieval optimization. Experience with vector databases such as Pinecone, Weaviate, Milvus, Chroma, or FAISS. Familiarity with Elasticsearch/OpenSearch and traditional relational or NoSQL databases. 4. Cloud and AI Deployment Experience deploying AI applications on AWS, Azure, or GCP. Familiarity with services such as AWS Bedrock, Azure OpenAI, or equivalent managed AI platforms. Experience with Docker, Kubernetes, CI/CD pipelines, and production monitoring. Understanding of scalability, performance optimization, model serving, and LLM cost management. 5. AI Quality, Security, and Governance Experience implementing LLM evaluation, hallucination mitigation, grounded responses, and AI guardrails. Knowledge of responsible AI, data privacy, secure API integration, and enterprise access controls. Understanding of observability, tracing, prompt/version management, and production incident troubleshooting. Wealth Management Domain Experience Experience working in wealth management, financial services, investment management, banking, or fintech. Understanding of portfolio management, asset allocation, investment research, financial advisors, and client servicing workflows. Familiarity with financial statements, investment reports, market data, and financial research documents. Ability to develop AI solutions that support advisors and operations teams while maintaining data confidentiality, traceability, and compliance requirements. Experience with regulated financial environments and human approval workflows for sensitive or consequential actions is a strong plus. Preferred Qualifications Bachelor's or Master's degree in Computer Science, Engineering, Artificial Intelligence, or a related field. 10+ years of overall software engineering experience, with substantial hands-on experience in AI/ML, LLM applications, or Generative AI. Demonstrated experience delivering AI solutions from proof of concept through production deployment. Strong analytical, problem-solving, communication, and stakeholder-management skills. Experience leading technical design discussions and mentoring engineering teams. Important: Candidates should demonstrate hands-on implementation of production-grade Agentic AI solutions, not just chatbot development, prompt engineering, or proof-of-concept projects.

Job Description Job Title: Cloud Solutions Architect / Cloud Engineer /Application Architect- AI Agentic, Integration & Architecture Location: New York, NY (Hybrid - 3 Days Onsite, 2 Days Remote) Duration: Long Term Contract Certification/Experience: Cloud certification and additional relevant certification Position Summary We are seeking a Senior Cloud Solutions Architect / Cloud Engineer/Application Architect with deep expertise in AI agentic architecture, enterprise integration, and architecture standards. This hands-on role designs agent-based and AI-enabled solutions, defines integration patterns, publishes application architecture standards, and helps establish an Architecture Center of Excellence (COE). The architect will work in Azure, AWS, and hybrid environments. 1. AI Agentic Architecture -Design reference architectures for agentic AI: multi-agent orchestration, tool calling, RAG, and human-in-the-loop controls. - Define secure patterns for integrating agents with enterprise systems and APIs (e.g., MCP, event-driven triggers). - Establish guardrails for agent identity, access, auditability, observability, evaluation, and cost. 2. Integration Architecture - Design and govern API-led, event-driven, and messaging integration patterns, including API lifecycle and gateway strategy. - Define integration patterns for legacy, SaaS, and cloud-native systems. 3. Standards & Architecture COE - Develop, publish, and maintain application, integration, API, and AI architecture standards and reference architectures. - Help establish and operate the COE: charter, governance forums, design reviews, training, and playbooks. - Define COE and standards-adoption metrics in coordination with AI IT adoption tracking. 4. Modernization, Security & Collaboration - Guide application modernization and cloud design on Azure, AWS, and hybrid environments. - Align designs with NIST (incl. AI RMF), CIS, ISO 27001, and security requirements. - Partner with product, engineering, DevOps, and Cybersecurity teams; communicate clearly to business and technical stakeholders. Qualifications 10+ years of application/enterprise architecture experience, including cloud-native design on Azure and/or AWS. Experience delivering AI/LLM or agentic solutions in an enterprise. Experience authoring standards and operating an architecture COE or review board. Strong integration/API expertise and executive-level communication

Job Description End Client: Metropolitan Transportation Authority (MTA) Job Title: Senior AI & Cloud Solutions Architect Duration: 12 months + Extension Location: New York, NY (Hybrid - 3 Days Onsite, 2 Days Remote) Number of Hours: 37.5 Hours a week Interview Type: Webcam or In-Person Ceipal ID: MTA_AI122_MA Job Code: 6122-1 Scope of Project: The MTA is seeking a Senior Cloud Solutions Architect / Cloud Engineer/Application Architect with deep expertise in AI agentic architecture, enterprise integration, and architecture standards. This hands-on role designs agent-based and AI-enabled solutions, defines integration patterns, publishes application architecture standards, and helps establish an Architecture Center of Excellence (COE). The architect will work in Azure, AWS, and hybrid environments. Responsibilities: 1. AI Agentic Architecture Design reference architectures for agentic AI: multi-agent orchestration, tool calling, RAG, and human-in-the-loop controls. Define secure patterns for integrating agents with enterprise systems and APIs (e.g., MCP, event-driven triggers). Establish guardrails for agent identity, access, auditability, observability, evaluation, and cost. 2. Integration Architecture Design and govern API-led, event-driven, and messaging integration patterns, including API lifecycle and gateway strategy. Define integration patterns for legacy, SaaS, and cloud-native systems. 3. Standards & Architecture COE Develop, publish, and maintain application, integration, API, and AI architecture standards and reference architectures. Help establish and operate the COE: charter, governance forums, design reviews, training, and playbooks. Define COE and standards-adoption metrics in coordination with AI IT adoption tracking. 4. Modernization, Security & Collaboration Guide application modernization and cloud design on Azure, AWS, and hybrid environments. Align designs with NIST (incl. AI RMF), CIS, ISO 27001, and MTA security requirements. Partner with product, engineering, DevOps, and Cybersecurity teams; communicate clearly to business and technical stakeholders. Required/Preferred Skills: 10+ years of application/enterprise architecture experience, including cloud-native design on Azure and/or AWS. Experience delivering AI/LLM or agentic solutions in an enterprise. Experience authoring standards and operating an architecture COE or review board. Strong integration/API expertise and executive-level communication. Cloud certification and additional relevant certification preferred. Expertise in agentic AI architecture, multi-agent orchestration, tool calling, RAG, and human-in-the-loop controls. Experience with enterprise systems and APIs, MCP, and event-driven triggers. Knowledge of API-led, event-driven, and messaging integration patterns, API lifecycle, and gateway strategy. Experience with legacy, SaaS, and cloud-native integration. Experience developing application, integration, API, and AI architecture standards and reference architectures. Experience establishing and operating an Architecture Center of Excellence (COE), including governance forums, design reviews, training, and playbooks. Knowledge of Azure, AWS, hybrid cloud environments, NIST AI RMF, CIS, ISO 27001, and enterprise security requirements. V Group Inc. is a NJ-based IT Services and Products Company with its business strategically categorized in various Business Units including Public Sector, Enterprise Solutions, Professional Services, Ecommerce, Projects, and Products. Within Public Sector business unit, we cater IT Professional Services to Federal, State and Local. We have multiple awards/ contracts with 30+ states, including but not limited to NY, CA, FL, GA, MD, MI, NC, OH, OR, CO, CT, TN, PA, TX, VA, NM, VT, and WA. If you are considering applying for a position with V Group, or in partnering with us on a position, please feel free to contact me for any questions you may have regarding our services and the advantages we can offer you as a consultant. Please share my contact information with others working in Information Technology. Website: LinkedIn: -group/ Facebook: Twitter:

Job Description Job Title: GenAI Architect Location: New York City, NY (Onsite/Local Candidates Preferred) Experience: 10+ Years Visa: Open to All Visa Types Job Summary: We are seeking an experienced GenAI Architect with 10+ years of overall experience to design, architect, and lead the delivery of enterprise-grade Generative AI solutions. The ideal candidate will have strong expertise in Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), Agentic AI workflows, and cloud-based AI architectures. The candidate will be responsible for translating business requirements into scalable, secure, and production-ready AI solutions while collaborating closely with business stakeholders, data scientists, and engineering teams. Key Responsibilities: Design and architect enterprise-grade Generative AI solutions leveraging LLMs, RAG pipelines, and agentic AI frameworks. Lead the development and implementation of scalable AI applications and intelligent automation solutions. Architect end-to-end RAG solutions, including document ingestion, chunking, embedding generation, vector databases, retrieval, and response generation. Design and implement agentic workflows using tools, APIs, orchestration frameworks, and multi-agent architectures. Evaluate and integrate LLM models from providers such as OpenAI, Anthropic, Google, and open-source ecosystems. Leverage cloud platforms such as AWS, Azure, or GCP to build and deploy enterprise AI solutions. Establish best practices for prompt engineering, context management, model evaluation, performance optimization, and LLM observability. Ensure AI solutions meet enterprise requirements for security, scalability, governance, compliance, and responsible AI. Collaborate with business stakeholders and technical teams to identify AI opportunities and translate business use cases into technical architectures. Provide technical leadership, architecture guidance, and mentorship to engineering teams throughout the development lifecycle. Required Technical Skills: 10+ years of overall experience in software engineering, solution architecture, AI/ML, or related technology domains. Strong hands-on experience designing and implementing Generative AI and LLM-based applications. Extensive experience with RAG architecture, vector databases, embeddings, semantic search, and knowledge retrieval systems. Strong understanding of Agentic AI, AI agents, multi-agent systems, and tool/function calling. Experience with frameworks such as LangChain, LangGraph, LlamaIndex, Semantic Kernel, or similar. Proficiency in Python and experience developing enterprise-grade applications and APIs. Experience working with LLM providers such as OpenAI, Azure OpenAI, Anthropic, Gemini, or open-source LLMs. Strong experience with at least one major cloud platform: AWS, Microsoft Azure, or Google Cloud Platform. Knowledge of vector databases such as Pinecone, Azure AI Search, FAISS, Weaviate, Milvus, or Chroma. Understanding of LLMOps, model deployment, monitoring, evaluation, and optimization. Experience with enterprise security, data privacy, access controls, and responsible AI practices. Preferred Qualifications: Experience architecting and delivering enterprise-scale GenAI solutions from concept to production. Knowledge of Kubernetes, Docker, microservices, and CI/CD pipelines. Experience with fine-tuning, model customization, and prompt optimization. Familiarity with AI governance, guardrails, hallucination mitigation, and responsible AI implementation. Excellent communication, stakeholder management, and technical leadership skills.

Job Description AI ML Engineer Intern - Winter / Spring 2027 (Master's or PhD) Salary: $25 - 30/ hr About Akoncagua AI Akoncagua AI builds domain-specialized AI systems for technically demanding industries. Our work includes LLMs, multimodal AI, agentic automation, RAG, evaluation, and production AI infrastructure. We are seeking an AI Automation Engineer Intern to help build secure, reliable AI agents and automated workflows for accounting and enterprise finance. What you'll work on Build and improve LLM agent workflows for document intake, classification, and transaction coding Develop document-intelligence pipelines for invoices, receipts, contracts, and financial statements Implement retrieval over accounting policies, vendor records, and historical transactions Write evaluation harnesses that measure extraction accuracy, classification quality, and end-to-end workflow outcomes Add tracing and observability for prompts, tool calls, failures, latency, and cost Integrate agents with accounting platforms, databases, and internal APIs Required qualifications Currently enrolled in a Master's or PhD program in Computer Science, AI/ML, or a related technical field, or equivalent practical experience Strong Python skills, plus solid fundamentals in data structures, debugging, and testing Experience building LLM-based applications, whether through coursework, research, personal projects, or internships Familiarity with at least one agent framework such as LangGraph, AutoGen/AG2, Semantic Kernel, or the OpenAI Agents SDK Working knowledge of RAG and vector databases Comfortable with Docker and version control in a collaborative codebase Preferred qualifications Experience with MCP or similar tool-integration standards Experience with n8n, Zapier, Make, or event-driven automation Exposure to FastAPI, PostgreSQL, pgvector, or Kubernetes Interest in or exposure to accounting concepts such as double-entry bookkeeping, journal entries, reconciliations, or approval controls Awareness of prompt-injection risks, least-privilege design, and secrets management Strong understanding of multi-GPU Linux server environments, with hands-on experience configuring, managing, and effectively utilizing GPU resources. Open-source contributions or publications at AI conferences How we work We care about researchers and engineers who understand and can defend the design decisions in their code. AI coding tools are welcome here, but you should be able to explain why a system works the way it does, what evidence supports it, and where it breaks. Our work connects model research with the systems required to run it. You will have room to explore new ideas, but you will also be expected to build working implementations, measure them carefully, and communicate their limitations clearly.

Job Description Job Title / Role: Enterprise Architect Key Skills: Enterprise Architecture, TOGAF, Agentic AI Architecture, Cloud Architecture Experience: 15+ Location: San Deigo CA Shift: General Mode: Remote We at Coforge are seeking "Enterprise Architect" with the following skill set: Key Responsibilities The Enterprise Architect is a senior consulting leader and trusted technology advisor who connects client business strategy with executable transformation, shapes commercially viable opportunities, and governs architecture outcomes across complex global ecosystems. Act as the trusted technology and AI advisor for assigned accounts, build C-suite relationships, lead discovery and maturity assessments, and provide independent, outcome-focused recommendations. Partner with account and sales leaders on account plans, opportunity identification, deal shaping, proposals and executive presentations while maintaining advisory integrity and ensuring appropriate AI interventions are included. Lead architecture and AI solutioning for strategic pursuits and transformation programmes, ensuring alignment with business objectives, enterprise principles and measurable value. Mentor Solution Architects, assure architecture and design quality, and provide senior technical leadership to resolve critical client and delivery issues. Architecture Development Define and maintain enterprise architecture principles, standards, reference architectures, reusable building blocks and governance across business, data, application and technology domains. Assess business strategy, operating models and the technology estate; develop capability maps, value streams, baseline and target architectures, transition states and pragmatic roadmaps. Apply a tailored method such as TOGAF ADM or Zachman framework, where appropriate, to organise viewpoints, assure completeness and maintain traceability. Translate target architectures into investment priorities, transformation increments, adoption plans and measurable benefits, KPIs and business outcomes. Develop enterprise and solution architectures for modernisation, cloud, integration, data and AI platforms, balancing reuse, interoperability, security, scalability, resilience, sustainability and cost. Guide build, buy and partner decisions, vendor and platform selection, and multi-vendor ecosystem integration while managing lock-in, sovereignty and regulatory considerations. Design for federated, multi-geography enterprises and global delivery models, recognising local regulations, organisational constraints and cultural differences. AI Expertise Architect enterprise AI, GenAI and Agentic AI solutions, including model and data integration, prompt orchestration, evaluation and AI operations for intelligent & autonomous enterprise. Define AI strategies, operating models, platform roadmaps, reference architectures and reusable patterns that convert business opportunities into executable transformation plans. Embed responsible AI, model governance, security, privacy, observability, resilience and cost controls into solution design and delivery. Guide AI solutions from proof of concept to secure, scalable production deployment with clear evaluation criteria and measurable business outcomes. Mentor architects and engineering teams in AI architecture, design, evaluation and client-facing technical leadership. Advisory Advise executives on technology strategy, operating-model implications and transformation priorities, including high-value AI opportunities. Lead discovery sessions, executive workshops, maturity assessments and option analyses that convert strategic objectives into practical investment roadmaps and value propositions. Present concise decision papers covering business value, investment choices, risks and trade-offs; challenge assumptions constructively and build executive alignment. Track market, industry and technology developments and adapt recommendations as client needs, regulations and ecosystem conditions evolve. Technology Ownership Own architecture outcomes for assigned accounts, domains and programmes, including strategic options, target-state decisions and delivery alignment. Lead Architecture Review Board activities and governance gates; maintain decision records, architecture contracts and traceability, and manage compliance, exceptions and waivers. Ensure target architectures are translated into funded and executable portfolios, with adoption, conformance, benefits and outcome tracking through delivery. Provide portfolio assurance across dependencies, interoperability, cyber risk, data sovereignty, regulatory compliance, resilience, technical debt and lifecycle health. Lead selected innovation initiatives and continuously improve EA effectiveness, maturity, methods and governance. Collaboration Influence without direct authority across client, SI, partner and vendor organisations, aligning business, sales, product, data, security, engineering and delivery leaders. Facilitate cross-domain and cross-cultural workshops, reconcile competing concerns, document trade-offs and secure agreement on principles, target states and priorities. Convert architecture commitments into executable delivery plans across distributed global teams while balancing strategic alignment, feasibility and stakeholder needs. Develop reusable consulting assets, industry viewpoints, reference architectures and accelerators, and contribute to the architecture community and thought leadership. Qualifications Bachelor's or Master's degree in Computer Science, Information Technology or a related discipline. 15+ years of technology experience, including 6-8+ years in enterprise or solution architecture within a global SI, leading consultancy or complex multinational enterprise. Demonstrated success leading executive advisory engagements, transformation programmes, technology account planning and strategic pursuits from discovery through delivery and value realisation. Strong architecture experience across distributed systems, integration, APIs, microservices, cloud, data, security, DevOps, resilience and performance engineering. Hands-on understanding of AI/ML platforms, GenAI and Agentic AI patterns, enterprise data foundations, model integration, RAG, evaluation, AI operations and responsible AI. Proficiency in TOGAF ADM and enterprise modelling; familiarity with LeanIX or similar EA tools, multi-vendor ecosystems and vendor/platform selection is desirable. Strong commercial judgement covering business value, investment options, total cost of ownership, deal economics, risk and architecture trade-offs. Excellent executive communication, storytelling, facilitation, negotiation and stakeholder-management skills, with proven ability to influence globally distributed teams without direct authority. Understanding of cyber security, privacy, regulatory compliance, data governance, sovereignty, operational resilience and sustainability. Preferred: TOGAF or equivalent EA certification; Agile/DevOps experience; and industry knowledge in Financial Services, Insurance, Travel and Transport, or other large-scale enterprise sectors.

Job Description About Quantiphi: Quantiphi is an award-winning, AI-First digital engineering and consulting company focused on delivering high-impact Services and Solutions that help organizations solve what truly matters. We partner with enterprises to reimagine their businesses through intelligent, scalable, and transformative AI driving measurable outcomes at the very core of their operations. Since our founding in 2013, Quantiphi has tackled some of the world's most complex business challenges by combining deep industry expertise, disciplined cloud and data engineering practices, and cutting-edge applied AI research. Our work is rooted in delivering accelerated, quantifiable business value, not just technology for technology's sake. Headquartered in Boston, Quantiphi is a global organization with 4,000+ professionals serving clients across key industry verticals, including BFSI, Healthcare & Life Sciences, CPG, MFG, TME etc. As an Elite and Premier partner to leading cloud and AI platforms such as NVIDIA, Google Cloud, AWS, and Snowflake, we build and deliver enterprise-grade AI services and solutions that create real-world impact. We've been recognized with: 21x Google Cloud Partner of the Year awards in the last 8 years. 3x AWS AI/ML award wins. 3x NVIDIA Partner of the Year titles. 2x Snowflake Partner of the Year awards. We have also garnered top analyst recognitions from Gartner, ISG, and Everest Group. We offer first-in-class industry solutions across Healthcare, Financial Services, Consumer Goods, Manufacturing, and more, powered by cutting-edge Generative AI and Agentic AI accelerators. We have been certified as a Great Place to Work for the third year in a row- 2021, 2022, 2023. Be part of a trailblazing team that's shaping the future of AI, ML, and cloud innovation. Your next big opportunity starts here! For more details, visit: Website or LinkedIn Page. Preferred Location: Charlotte, NC Experience Level: 10+ Years of Experience Role purpose: Design, develop and harden a Universal Gateway that connects conversational AI agents to enterprise applications. The Gateway will manage conversation state, enforce business rules, execute authorized backend operations and return the data and guidance needed by the conversational agent. Key responsibilities: Design and implement Gateway APIs used by Google CXAS agents, including session initialization, journey resolution, state transitions and business-operation execution. Implement an external state engine covering current journey, active state, required information, pending actions, customer confirmation and completion status. Define versioned journey configurations, transition rules, policies and state-specific response guidance. Ensure the Gateway validates actions proposed by an agent against current state, identity, authorization and business rules. Develop adapters for customer backend systems using approved API contracts. Implement timeouts, retries, idempotency, concurrency controls and recovery from partial failures. Persist session state and audit records with appropriate isolation, retention and protection of customer data. Implement authentication, authorization, secrets management and service-to-service security. Provide correlated traces showing the incoming request, policy decision, state before and after, backend operation and response. Integrate automated tests and AgentCert evidence into the development workflow. Package and deploy services to the agreed Google Cloud environment, including Cloud Run where selected. Produce API documentation, architecture decisions, operational runbooks and handover material. Required skills: Strong production backend development experience in Python, Java preferably FastAPI or an equivalent framework. REST APIs, OpenAPI specifications, JSON Schema and typed request/response validation. Practical experience implementing workflow engines, finite-state machines or business-process orchestration. Distributed systems concepts: Concurrency, retries, timeouts and transaction boundaries. Experience with persistent data stores and session management. Google Cloud delivery experience, including IAM, containerized services, logging, monitoring and secrets management. Enterprise API integration and automated unit, integration and contract testing. Understanding of LLM tool calling and the distinction between model-generated intent and authorized business execution. Git, code reviews and CI/CD. Preferred skills: Telecom billing & background, customer service or service-restoration processes. Integration with Google CXAS or another conversational AI platform. Agent evaluation, failure injection, load testing and observability. Experience replacing synthetic adapters with enterprise backend integrations. What is in it for you: Be part of the fastest-growing AI-first digital transformation and engineering company in the world Be a leader of an energetic team of highly dynamic and talented individuals Exposure to working with fortune 500 companies and innovative market disruptors Exposure to the latest technologies related to artificial intelligence and machine learning, data and cloud

McKesson is an impact-driven, Fortune 10 company that touches virtually every aspect of healthcare. We are known for delivering insights, products, and services that make quality care more accessible and affordable. Here, we focus on the health, happiness, and well-being of you and those we serve - we care. What you do at McKesson matters. We foster a culture where you can grow, make an impact, and are empowered to bring new ideas. Together, we thrive as we shape the future of health for patients, our communities, and our people. If you want to be part of tomorrow's health today, we want to hear from you. Role Overview The Lead Business Strategic Insights role partners closely with leaders across Commercial Strategy & Innovation (CSI) to develop data-driven solutions that accelerate business growth, enhance customer outcomes, and bring innovative products to market. This individual will leverage advanced analytics, artificial intelligence, machine learning, and business insights to solve complex challenges, uncover opportunities, and drive strategic decision-making across the organization. This role will participate throughout the full solution lifecycle, from ideation and proof of concept through production deployment and business adoption. Success in this position requires a combination of analytical expertise, business acumen, technical proficiency, and stakeholder influence. Key Responsibilities Accelerate AI-enabled decision-making by partnering with data governance, product, and technology teams to leverage governed data assets, semantic layers, and AI/LLM technologies that enable business users to access trusted insights more efficiently. Design and develop heuristics, optimization models, and advanced analytics solutions across customer, product, and operational domains to improve business outcomes. Utilize qualitative and quantitative data to identify opportunities, develop hypotheses, pilot proof-of-concept solutions, and scale successful models across the enterprise. Apply data science and analytical methodologies to identify market trends, process improvement opportunities, strategic synergies, and actionable business recommendations. Support digital data acquisition and integration strategies while building scalable data pipelines that complement existing enterprise data assets. Leverage CSI's data infrastructure to develop next-generation analytical models focused on customer value creation, margin expansion, and operational efficiencies. Drive experimentation and causal learning through pilots, A/B testing, and other measurement methodologies to quantify business impact and improve decision-making. Break down complex business challenges into scalable workstreams and collaborate across functional teams to drive solution adoption and implementation. Develop and deploy scalable Python-based APIs and data products that integrate with enterprise applications and business systems. Build and scale AI agents that enable business users to interact with data using natural language, uncover insights, identify opportunities, and make faster, more informed decisions. Present findings, recommendations, and business cases to senior leaders and cross-functional stakeholders. Minimum Requirements Bachelor's degree in Business, Data Science, Computer Science, Statistics, Engineering, Mathematics, or a related field, or equivalent experience. Typically requires 7+ years of relevant professional experience in analytics, data science, business intelligence, strategy, or a related discipline. Critical Skills 5+ years of experience in analytics, data science, business intelligence, or a related field. Proven experience leveraging analytics to drive measurable business value and strategic outcomes. Strong understanding of software engineering principles and API development used to commercialize analytical products and solutions. Advanced communication, presentation, and stakeholder management skills. Hands-on experience with Python, including libraries such as Pandas, Scikit-learn, NumPy, and related tools. Advanced SQL skills with experience querying and manipulating large, complex datasets. Strong understanding of machine learning techniques, including clustering, regression, anomaly detection, forecasting, classification, deep learning, and time series analysis. Experience working with cloud-based data platforms and modern analytics ecosystems. Experience building, deploying, or leveraging AI and generative AI solutions within business environments. Preferred Qualifications Experience working in healthcare, distribution, pharmaceutical, or supply chain environments. Experience with experimental design, causal inference, and business impact measurement. Familiarity with modern AI frameworks, large language models (LLMs), and agentic AI solutions. Experience building scalable data products and deploying machine learning models into production environments. Demonstrated success influencing business decisions through analytics and strategic insights. Additional Skills Passion for using data to solve complex business problems. Strong business acumen and strategic thinking capabilities. Ability to balance technical depth with business priorities. Self-starter with a continuous improvement mindset. Strong collaboration and relationship-building skills across functions and leadership levels. This role is a hybrid position. The selected candidate is expected to work on-site at our Las Colinas office a minimum of two (2) days per week, with the remaining days worked remotely. Specific in-office days may be designated based on team needs and business priorities. We are proud to offer a competitive compensation package at McKesson as part of our Total Rewards. This is determined by several factors, including performance, experience and skills, equity, regular job market evaluations, and geographical markets. The pay range shown below is aligned with McKesson's pay philosophy, and pay will always be compliant with any applicable regulations. In addition to base pay, other compensation, such as an annual bonus or long-term incentive opportunities may be offered. For more information regarding benefits at McKesson, please click here. Our Base Pay Range for this position $136,300 - $227,100 McKesson has become aware of online recruiting-related scams in which individuals who are not affiliated with or authorized by McKesson are using McKesson's (or affiliated entities, like CoverMyMeds or RxCrossroads) name in fraudulent emails, job postings or social media messages. In light of these scams, please bear the following in mind: McKesson Talent Advisors will never solicit money or credit card information in connection with a McKesson job application. McKesson Talent Advisors do not communicate with candidates via online chatrooms or using email accounts such as Gmail or Hotmail. Note that McKesson does rely on a virtual assistant (Gia) for certain recruiting-related communications with candidates. McKesson job postings are posted on our career site: . McKesson is an Equal Opportunity Employer McKesson provides equal employment opportunities to applicants and employees, without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, protected veteran status, disability, age, genetic information, or any other legally protected category. For additional information on McKesson's full Equal Employment Opportunity policies, visit our Equal Employment Opportunity page. McKesson is committed to being an Equal Employment Opportunity Employer and offers opportunities to all job seekers including job seekers with disabilities. If you need a reasonable accommodation to assist with your job search or application for employment, please contact us by sending an email to (United States) or (Canada) . Resumes or CVs submitted to this email box will not be accepted. Join us at McKesson!

AI Engineer 3 (AI Foundations) 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. Lead development and benchmarking of multi-turn conversational and tool-using agent workflows, ensuring measurable performance and safety metrics Implement scalable pipelines for training, fine-tuning and deploying foundation or domain-specific models across multiple environments Collaborate with research and data engineering teams to curate high-quality datasets and improve model evaluation methodologies Contribute to governance and security efforts for model traceability, lineage documentation, and version control of deployed AI assets Mentor junior AI engineers and advocate for engineering excellence, reproducibility, and responsible experimentation 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 This role is designated as a hybrid position. You are expected to adhere to Capital One's hybrid policy regarding required on-site presence at your designated office location. Basic Qualifications: Bachelor's Degree in Computer Science, AI, Electrical Engineering, Computer Engineering, or related fields plus at least 3 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 1 year of experience developing AI and ML algorithms or technologies At least 3 years of experience programming with Python, Go, Scala, CUDA, or Java Preferred Qualifications: Experience contributing to development of components of AI systems with tradeoff decisions around cost, latency, throughput and accuracy 4+ years of experience deploying scalable and responsible AI solutions on cloud platforms (e.g. AWS, Google Cloud, Azure, or equivalent private cloud) Experience developing, delivering, and supporting AI services 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 Demonstrated ability to evaluate and optimize LLM performance using quantitative metrics (accuracy, coherence, latency, cost) Hands-on experience implementing retrieval-augmented generation (RAG), vector database integrations, and fine-tuning workflows Experience applying prompt-engineering strategies, safety guardrails, and red-teaming methodologies to production AI systems Capital One will consider sponsoring a new qualified applicant for employment authorization for this position. R AI Engineer 3 (AI Foundations) 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. McLean, VA: $161,800 - $184,600 for AI Engineer 3 New York, NY: $176,500 - $201,400 for AI Engineer 3 San Jose, CA: $176,500 - $201,400 for AI Engineer 3 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, any position posted in the United Kingdom is for Capital One Europe and any position posted in the Philippines is for Capital One Philippines Service Corp. (COPSSC).

AI Engineer 3 (AI Foundations) 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. Lead development and benchmarking of multi-turn conversational and tool-using agent workflows, ensuring measurable performance and safety metrics Implement scalable pipelines for training, fine-tuning and deploying foundation or domain-specific models across multiple environments Collaborate with research and data engineering teams to curate high-quality datasets and improve model evaluation methodologies Contribute to governance and security efforts for model traceability, lineage documentation, and version control of deployed AI assets Mentor junior AI engineers and advocate for engineering excellence, reproducibility, and responsible experimentation 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 This role is designated as a hybrid position. You are expected to adhere to Capital One's hybrid policy regarding required on-site presence at your designated office location. Basic Qualifications: Bachelor's Degree in Computer Science, AI, Electrical Engineering, Computer Engineering, or related fields plus at least 3 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 1 year of experience developing AI and ML algorithms or technologies At least 3 years of experience programming with Python, Go, Scala, CUDA, or Java Preferred Qualifications: Experience contributing to development of components of AI systems with tradeoff decisions around cost, latency, throughput and accuracy 4+ years of experience deploying scalable and responsible AI solutions on cloud platforms (e.g. AWS, Google Cloud, Azure, or equivalent private cloud) Experience developing, delivering, and supporting AI services 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 Demonstrated ability to evaluate and optimize LLM performance using quantitative metrics (accuracy, coherence, latency, cost) Hands-on experience implementing retrieval-augmented generation (RAG), vector database integrations, and fine-tuning workflows Experience applying prompt-engineering strategies, safety guardrails, and red-teaming methodologies to production AI systems Capital One will consider sponsoring a new qualified applicant for employment authorization for this position. R AI Engineer 3 (AI Foundations) 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. McLean, VA: $161,800 - $184,600 for AI Engineer 3 New York, NY: $176,500 - $201,400 for AI Engineer 3 San Jose, CA: $176,500 - $201,400 for AI Engineer 3 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, any position posted in the United Kingdom is for Capital One Europe and any position posted in the Philippines is for Capital One Philippines Service Corp. (COPSSC).

Job Title: Enterprise AI Architect - GenAI / Agentic AI / RAG / AI Governance Location: Plano, TX (Local candidates only. Hybrid, 3 days onsite per week required.) Duration: Contract - 12 months Pay Range: $73.67/hr (W2) Job ID: 412083 About BCforward BCforward is a leading global IT consulting and workforce solutions firm providing services and support to Fortune 500 and government clients. Founded in 1998, BCforward has grown with our customers needs into a full-service business solutions provider. With delivery centers and offices across North America and India, we take pride in building long-term relationships and delivering excellence through innovation, collaboration, and integrity. Job Description We are seeking an experienced Enterprise AI Architect - GenAI / Agentic AI / RAG / AI Governance to serve as a senior AI architecture leader responsible for defining enterprise patterns, standards, and decision frameworks that guide the adoption of AI across Global Technology. The ideal candidate will demonstrate strong experience in AI platforms, agentic systems, retrieval architectures, model integration, workflow automation, and enterprise AI governance, with a proven ability to translate rapidly evolving AI capabilities into practical, scalable, and governed enterprise solutions while partnering closely with CIO architecture teams, platform engineering, security, and risk. Responsibilities: Define enterprise AI architecture strategy, patterns, and reference architectures. Establish decision frameworks for AI platform selection, solution design, and deployment approaches. Develop governance models, standards, and guardrails for AI and agentic solutions. Drive consistency and reuse through enterprise AI patterns, templates, and guidance artifacts. Align AI architecture with enterprise security, risk, compliance, and control requirements. Define architecture patterns for enterprise AI agents, multi-agent systems, and workflow automation. Establish guidance for agent orchestration, planning, memory, tool invocation, and human oversight. Drive standards for deterministic, probabilistic, and hybrid automation models. Define integration patterns for agent-to-agent communication, tool usage, and enterprise workflows. Guide adoption of emerging AI interoperability standards and architectures, including MCP where applicable. Develop decision guidance for model selection, orchestration approaches, and AI solution architectures. Partner with platform teams to align enterprise AI capabilities with business and technology needs. Define patterns for RAG, vector databases, semantic retrieval, and enterprise knowledge architectures. Guide architecture decisions for AI-ready data access, grounding, and contextual retrieval. Guide architecture decisions involving AI Gateway, API Gateway, and MCP Gateway capabilities. Embed security through architecture patterns covering identity, access, data protection, and policy enforcement. Translate complex AI concepts into clear, actionable guidance for technical and business audiences. Communicate effectively with senior stakeholders on business value, risk, operating models, and technology choices. Act as a senior AI architecture leader driving alignment across multiple organizations and initiatives. Promote responsible AI adoption through practical governance and architectural guardrails. Required Skills & Qualifications: 15+ years in enterprise architecture, solution architecture, software engineering, or distributed systems design. 8+ years architecting AI/ML, NLP, or advanced analytics solutions. 2+ years designing enterprise GenAI, RAG, and agentic AI solutions. Strong knowledge of LLMs, RAG, embeddings, vector databases, evaluation frameworks, and agent-based architectures. Experience with enterprise AI platforms such as Microsoft Copilot Studio, Azure AI services, and Azure OpenAI or equivalents. Familiarity with AI orchestration frameworks, workflow automation, and enterprise integration patterns. Understanding of security, governance, privacy, and risk considerations for AI solutions. Proven ability to influence senior stakeholders and drive architecture decisions in regulated environments. Strong communication skills with the ability to simplify complex AI concepts and drive alignment across technical and business audiences. Preferred Skills: Experience with agentic AI, multi-agent systems, MCP, and emerging AI interoperability standards. Experience with Microsoft AI ecosystem capabilities including Copilot Studio, Azure AI Foundry, Azure OpenAI, and GitHub Copilot. Experience with AWS AI services including Amazon Bedrock, SageMaker, and AI application architectures. Knowledge of knowledge graphs, semantic search, vector databases, and enterprise retrieval architectures. Experience defining AI governance, model lifecycle management, and responsible AI practices. Experience operating in large-scale, highly regulated enterprise environments. Why BCforward? At BCforward, we believe in advancing lives and careers. When you join our team, you gain access to: Competitive compensation and benefits. Opportunities for growth with global clients. A supportive, inclusive culture that values innovation and people. Exposure to cutting-edge technologies and projects. About Our Commitment BCforward is an equal opportunity employer. We value diversity and are committed to creating an inclusive environment for all employees. All qualified applicants will receive consideration for employment without regard to race, color, religion, gender, sexual orientation, gender identity, national origin, age, disability, or veteran status. Interested? Apply Now! If this sounds like the right opportunity for you, please apply with your most recent resume.