Software Solutions Architect Direct hire Hybrid - Dublin, OH or Orlando, FL JOB SUMMARY An Software Architect is a senior technical leader who shapes and drives the architecture of commercial technologies across the company and is influential across multiple engineering teams. They are a hands-on technologist who leads by example and coaches engineers in modern, AI-augmented engineering practices. They are an expert at designing all layers of an application and platform, and they pioneer the use of Agentic AI for architecture, technical design, and implementation-using AI agents to accelerate solution design, generate and review code, evaluate trade-offs, and automate repetitive engineering tasks. They work in an Agile development environment architecting, designing, leading, and delivering technology solutions to transform healthcare into a safer and more cost-effective industry. They are expected to be highly in-tune with industry best practices, emerging AI tooling, technologies, processes, and techniques, and can provide leadership to multiple teams on how to apply them pragmatically to solve business problems. ESSENTIAL DUTIES & RESPONSIBILITIES Software Development • Set organization-wide standards for software structure, maintainability, and quality, including standards for the responsible use of Agentic AI in implementation. • Excels in meeting both explicit and implicit user needs. • Act as a thought leader in software engineering and AI-augmented development-designing prompts, guardrails, and agent workflows that accelerate delivery without compromising quality. • Teach others to solve problems using multiple languages, technologies, software tools, and AI coding agents. • Serve as subject matter expert (SME) in several technologies critical to the organization and contribute to setting organizational standards in technology, methodology, and Agentic AI tooling. Testing & Quality Assurance • Master of comprehensive testing strategies that include microservices architecture, API contract testing, and end-to-end workflows. • Apply Agentic AI to author, expand, and maintain test suites, generate edge-case scenarios, and triage failures. • Provide organizational guidance on testing best practices, including test automation, CI/CD integration, AI-assisted quality engineering, and metrics tracking. Data Management • Partner with other expertise sets to provide prescriptive and anticipatory intelligence. • Design solutions that efficiently interact with advanced machine learning tools, generative AI services, and cloud-based data services. • Architect data flows that power Agentic AI use cases (retrieval, grounding, evaluation). • Set organizational best practices for data management and is considered a Subject Matter Expert. Infrastructure & Integrations • Mastery of cloud architecture, designing and guiding the implementation and adoption of cloud-native solutions across the organization. • Set best practices for cloud service utilization, data migration, complex system integration strategies, and the deployment of AI agents and LLM-backed services. • Provide technical direction and governance on advanced infrastructure and integrations including resource optimization and AI workload cost management. Operations • Review analytics to understand user behavior and identify trends in operational metrics to suggest architectural changes. • Define organizational strategies for operations, including AI-assisted observability, incident response, and self-healing systems. • Subject Matter Expert in operational best practices, tools, and techniques. • Understand business impact at a macro level and advocate for protecting company reputation. Security & Compliance • Direct teams in multi-stage threat mitigation and secure coding practices, including secure use of Agentic AI (prompt injection defense, model output validation, data governance for AI agents). • Develop tools to mediate security holes and offer advanced regulatory compliance guidance, especially as it relates to AI in regulated healthcare contexts (HIPAA, HITRUST). • Set the standard for security and compliance within the organization. Product & User Experience • Shape the organization's user-centric direction and champion seamless experiences, including AI-native interactions where appropriate. • Define user interaction patterns for multiple products, fostering consistent experiences. • Demonstrate deep expertise in user-facing technologies, advocating for best practices and innovations. • Lead in prioritizing and addressing tech and design debt across the platform. Requirements & Design • Set industry standards in crafting functional and non-functional requirements. • Orchestrate complex system architectures. • Fully understand the path of data. • Manage the balance of engineering future and current needs. • Set best practices and anti-patterns for the organization. • Apply Agentic AI to accelerate architecture work-generating design alternatives, evaluating trade-offs, producing ADRs, and reviewing designs against established patterns and constraints. KNOWLEDGE & REQUIREMENTS • Highly motivated, self-learner, and technically inquisitive, with a strong bias toward applying emerging AI tooling to engineering problems. • Significant experience creating architecture for complex distributed systems and leading teams through development of those systems. • Hands-on experience using Agentic AI for architecture, technical design, and implementation (e.g., AI coding agents, design copilots, automated code review, RAG-based design assistants). • Capable of leading and/or participating in business, culture, technical, and practice initiatives that support continuous improvement across the organization, including driving adoption of AI-augmented engineering workflows. • Excellent track record of collaborating with stakeholders. Experience proposing new product offerings and contributing to company-wide strategies using industry knowledge and foresight. • Negotiates complex or risky technical business issues on behalf of the company. • Consults with management to determine project objectives with long-term implications. • Experience leading technical due diligence and road mapping for strategic partnerships, third-party partners, mergers, and acquisitions. • Effective communication and leadership capabilities: capable of communicating strategy and influencing others to align to future state architecture. • Expert level experience with multiple client-side and server-side programming languages (Java with Spring Boot, JavaScript React experience preferred). • Expert level experience developing, deploying, and supporting REST services and microservices. • Expert level experience with container orchestration platforms and cloud-native architectures; ability to design and lead adoption of cloud services across major cloud providers regardless of specific platform. • Working knowledge of LLM-backed services, prompt engineering, agent frameworks, evaluation harnesses, and the governance considerations that come with them. • Significant experience with relational databases, non-relational databases, messaging systems, and event-driven architecture patterns. • Expert level experience with containerization technologies and container-based deployment strategies. • Deep expertise in infrastructure as code (IaC) practices to enable repeatable, auditable, and scalable infrastructure. • Strong command of service mesh and API gateway patterns for securing and managing microservices at scale. • Experience with GitOps and modern continuous delivery practices for cloud-native platforms. • Capable of joining a team to lead initiatives and create software to solve challenging problems. EDUCATION & EXPERIENCE REQUIREMENTS • Minimum years of work experience: 8+ years (including demonstrated experience leveraging Agentic AI for architecture, technical design, and implementation) • Minimum level of education or education/experience: Bachelor's or Advanced Degree in related field or equivalent work experience Determining compensation for this role (and others) at Vaco/Highspring depends upon a wide array of factors including but not limited to the individual's skill sets, experience and training, licensure and certifications, office location and other geographic considerations, as well as other business and organizational needs. With that said, as required by local law in geographies that require salary range disclosure, Vaco/Highspring notes the salary range for the role is noted in this job posting. The individual may also be eligible for discretionary bonuses, and can participate in medical, dental, and vision benefits as well as the company's 401(k) retirement plan. Additional disclaimer: Unless otherwise noted in the job description, the position Vaco/Highspring is filing for is occupied. Please note, however, that Vaco/Highspring is regularly asked to provide talent to other organizations. By submitting to this position, you are agreeing to be included in our talent pool for future hiring for similarly qualified positions. Submissions to this position are subject to the use of AI to perform preliminary candidate screenings, focused on ensuring minimum job requirements noted in the position are satisfied. Further assessment of candidates beyond this initial phase within Vaco/Highspring will be otherwise assessed by recruiters and hiring managers. Vaco/Highspring does not have knowledge of the tools used by its clients in making final hiring decisions and cannot opine on their use of AI products. EEO Notice Vaco by Highspring is an Equal Opportunity Employer and does not discriminate against any employee or applicant for employment because of race (including but not limited to traits historically associated with race such as hair texture and hair style), color, sex (includes pregnancy or related conditions) . click apply for full job details
09/24/2026
Full time
Software Solutions Architect Direct hire Hybrid - Dublin, OH or Orlando, FL JOB SUMMARY An Software Architect is a senior technical leader who shapes and drives the architecture of commercial technologies across the company and is influential across multiple engineering teams. They are a hands-on technologist who leads by example and coaches engineers in modern, AI-augmented engineering practices. They are an expert at designing all layers of an application and platform, and they pioneer the use of Agentic AI for architecture, technical design, and implementation-using AI agents to accelerate solution design, generate and review code, evaluate trade-offs, and automate repetitive engineering tasks. They work in an Agile development environment architecting, designing, leading, and delivering technology solutions to transform healthcare into a safer and more cost-effective industry. They are expected to be highly in-tune with industry best practices, emerging AI tooling, technologies, processes, and techniques, and can provide leadership to multiple teams on how to apply them pragmatically to solve business problems. ESSENTIAL DUTIES & RESPONSIBILITIES Software Development • Set organization-wide standards for software structure, maintainability, and quality, including standards for the responsible use of Agentic AI in implementation. • Excels in meeting both explicit and implicit user needs. • Act as a thought leader in software engineering and AI-augmented development-designing prompts, guardrails, and agent workflows that accelerate delivery without compromising quality. • Teach others to solve problems using multiple languages, technologies, software tools, and AI coding agents. • Serve as subject matter expert (SME) in several technologies critical to the organization and contribute to setting organizational standards in technology, methodology, and Agentic AI tooling. Testing & Quality Assurance • Master of comprehensive testing strategies that include microservices architecture, API contract testing, and end-to-end workflows. • Apply Agentic AI to author, expand, and maintain test suites, generate edge-case scenarios, and triage failures. • Provide organizational guidance on testing best practices, including test automation, CI/CD integration, AI-assisted quality engineering, and metrics tracking. Data Management • Partner with other expertise sets to provide prescriptive and anticipatory intelligence. • Design solutions that efficiently interact with advanced machine learning tools, generative AI services, and cloud-based data services. • Architect data flows that power Agentic AI use cases (retrieval, grounding, evaluation). • Set organizational best practices for data management and is considered a Subject Matter Expert. Infrastructure & Integrations • Mastery of cloud architecture, designing and guiding the implementation and adoption of cloud-native solutions across the organization. • Set best practices for cloud service utilization, data migration, complex system integration strategies, and the deployment of AI agents and LLM-backed services. • Provide technical direction and governance on advanced infrastructure and integrations including resource optimization and AI workload cost management. Operations • Review analytics to understand user behavior and identify trends in operational metrics to suggest architectural changes. • Define organizational strategies for operations, including AI-assisted observability, incident response, and self-healing systems. • Subject Matter Expert in operational best practices, tools, and techniques. • Understand business impact at a macro level and advocate for protecting company reputation. Security & Compliance • Direct teams in multi-stage threat mitigation and secure coding practices, including secure use of Agentic AI (prompt injection defense, model output validation, data governance for AI agents). • Develop tools to mediate security holes and offer advanced regulatory compliance guidance, especially as it relates to AI in regulated healthcare contexts (HIPAA, HITRUST). • Set the standard for security and compliance within the organization. Product & User Experience • Shape the organization's user-centric direction and champion seamless experiences, including AI-native interactions where appropriate. • Define user interaction patterns for multiple products, fostering consistent experiences. • Demonstrate deep expertise in user-facing technologies, advocating for best practices and innovations. • Lead in prioritizing and addressing tech and design debt across the platform. Requirements & Design • Set industry standards in crafting functional and non-functional requirements. • Orchestrate complex system architectures. • Fully understand the path of data. • Manage the balance of engineering future and current needs. • Set best practices and anti-patterns for the organization. • Apply Agentic AI to accelerate architecture work-generating design alternatives, evaluating trade-offs, producing ADRs, and reviewing designs against established patterns and constraints. KNOWLEDGE & REQUIREMENTS • Highly motivated, self-learner, and technically inquisitive, with a strong bias toward applying emerging AI tooling to engineering problems. • Significant experience creating architecture for complex distributed systems and leading teams through development of those systems. • Hands-on experience using Agentic AI for architecture, technical design, and implementation (e.g., AI coding agents, design copilots, automated code review, RAG-based design assistants). • Capable of leading and/or participating in business, culture, technical, and practice initiatives that support continuous improvement across the organization, including driving adoption of AI-augmented engineering workflows. • Excellent track record of collaborating with stakeholders. Experience proposing new product offerings and contributing to company-wide strategies using industry knowledge and foresight. • Negotiates complex or risky technical business issues on behalf of the company. • Consults with management to determine project objectives with long-term implications. • Experience leading technical due diligence and road mapping for strategic partnerships, third-party partners, mergers, and acquisitions. • Effective communication and leadership capabilities: capable of communicating strategy and influencing others to align to future state architecture. • Expert level experience with multiple client-side and server-side programming languages (Java with Spring Boot, JavaScript React experience preferred). • Expert level experience developing, deploying, and supporting REST services and microservices. • Expert level experience with container orchestration platforms and cloud-native architectures; ability to design and lead adoption of cloud services across major cloud providers regardless of specific platform. • Working knowledge of LLM-backed services, prompt engineering, agent frameworks, evaluation harnesses, and the governance considerations that come with them. • Significant experience with relational databases, non-relational databases, messaging systems, and event-driven architecture patterns. • Expert level experience with containerization technologies and container-based deployment strategies. • Deep expertise in infrastructure as code (IaC) practices to enable repeatable, auditable, and scalable infrastructure. • Strong command of service mesh and API gateway patterns for securing and managing microservices at scale. • Experience with GitOps and modern continuous delivery practices for cloud-native platforms. • Capable of joining a team to lead initiatives and create software to solve challenging problems. EDUCATION & EXPERIENCE REQUIREMENTS • Minimum years of work experience: 8+ years (including demonstrated experience leveraging Agentic AI for architecture, technical design, and implementation) • Minimum level of education or education/experience: Bachelor's or Advanced Degree in related field or equivalent work experience Determining compensation for this role (and others) at Vaco/Highspring depends upon a wide array of factors including but not limited to the individual's skill sets, experience and training, licensure and certifications, office location and other geographic considerations, as well as other business and organizational needs. With that said, as required by local law in geographies that require salary range disclosure, Vaco/Highspring notes the salary range for the role is noted in this job posting. The individual may also be eligible for discretionary bonuses, and can participate in medical, dental, and vision benefits as well as the company's 401(k) retirement plan. Additional disclaimer: Unless otherwise noted in the job description, the position Vaco/Highspring is filing for is occupied. Please note, however, that Vaco/Highspring is regularly asked to provide talent to other organizations. By submitting to this position, you are agreeing to be included in our talent pool for future hiring for similarly qualified positions. Submissions to this position are subject to the use of AI to perform preliminary candidate screenings, focused on ensuring minimum job requirements noted in the position are satisfied. Further assessment of candidates beyond this initial phase within Vaco/Highspring will be otherwise assessed by recruiters and hiring managers. Vaco/Highspring does not have knowledge of the tools used by its clients in making final hiring decisions and cannot opine on their use of AI products. EEO Notice Vaco by Highspring is an Equal Opportunity Employer and does not discriminate against any employee or applicant for employment because of race (including but not limited to traits historically associated with race such as hair texture and hair style), color, sex (includes pregnancy or related conditions) . click apply for full job details
About Us Visa is a world leader in payments technology, facilitating transactions between consumers, merchants, financial institutions and government entities across more than 200 countries and territories, dedicated to uplifting everyone, everywhere by being the best way to pay and be paid. At Visa, you'll have the opportunity to create impact at scale - tackling meaningful challenges, growing your skills and seeing your contributions impact lives around the world. Join Visa and do work that matters - to you, to your community, and to the world. Progress starts with you. Job Description Role Summary: The Sr. Manager; Software Engineering leads a team responsible for reviewing business requirements and developing functional and technical design documentation. This role oversees the design and implementation of testing procedures for APIs, abstractions, and integration patterns to address distributed computing challenges, including end-to-end and integration testing. The Sr. Manager participates in design reviews, providing input on requirements, product designs, schedules, and potential issues, and works across the team to ensure productivity, predictability, and delivery of high-quality results. The position includes accountability for code review and the team's development and testing activities, as well as participation in proof of concepts and technical evaluations of new technologies. The Sr. Manager is expected to foster a collaborative environment, ensuring the team meets business needs and delivers robust solutions. All roles require digital fluency, including the ability to work with emerging technologies such as Generative AI tools (e.g. ChatGPT, Microsoft Copilot) and agentic development to support everyday work. Essential Functions: People Leadership: Direct, mentor, and build a high-performing team of 8+ software engineers. Drive career development, performance management, regular 1:1s, and goal setting. Team Execution & Delivery: Oversee end-to-end application development deliverables including unit design, coding, code reviews, testing, and release management. Resource & Capacity Planning: Manage team workload, sprint planning, hiring, onboarding, and resource allocation to meet project timelines and deliverables. Technical & Strategic Guidance: Collaborate with technical leads, architects, and product managers to define solution approaches, assess scope, and evaluate technical feasibility. Agile Leadership: Drive agile development workflows utilizing modern open-source, CI/CD, and DevOps tools to streamline continuous integration and delivery pipelines. Cross-Functional Collaboration: Partner closely with global cross-functional teams, including product management, QA, security, and operations, to resolve blockers and ensure aligned delivery. Compliance & Quality: Ensure development adheres to Visa Development Management Methodology, Technical Security Requirements, and Secure Software Development Lifecycle (SSDLC) protocols. Documentation & Process Improvement: Maintain clear software, operational, and architectural documentation while continually improving engineering practices and procedures. Visa requires at least 3 days in office, expectations of these days will be confirmed by your Hiring Manager. Qualifications Basic Qualifications: 8+ years of relevant work experience and a Bachelors degree, OR 11+ years of relevant work experience. Experience building and supporting large-scale, high-performance, mission-critical applications. Strong hands-on software engineering experience, including coding, code reviews, and technical design. Proven ability to lead technical initiatives and drive architecture decisions. Experience with cloud-native technologies, distributed systems, APIs, and microservices. Experience supporting Tier-0 or highly available production systems. Ability to mentor engineers and provide technical leadership across engineering teams. Strong understanding of software engineering best practices, including CI/CD, testing, observability, and operational excellence Preferred Qualifications: 9 or more years of relevant work experience with a Bachelor Degree or 7 or more relevant years of experience with an Advanced Degree (e.g. Masters, MBA, JD, MD) or 3 or more years of experience with a PhD. 3+ years of direct people-management experience preferred; candidates who have led teams through technical influence, matrix leadership, or technical leadership without direct authority will also be considered Demonstrated experience leading software engineering teams, including delivery management, technical oversight, performance accountability, and talent development. Experience translating business and product requirements into scalable technical solutions, architecture decisions, delivery plans, and technical documentation. Proven ability to lead the design, development, testing, integration, deployment, and production support of complex, mission-critical applications. Strong experience overseeing API platforms, distributed systems, microservices, and enterprise integration patterns. Experience leading architecture and design reviews, evaluating technical tradeoffs, and ensuring solutions meet requirements for scalability, reliability, security, and performance. Experience establishing engineering standards and maintaining accountability for code quality, testing, secure development, release readiness, and production stability. Proven ability to manage delivery risks, dependencies, capacity, and priorities across multiple teams and cross-functional stakeholders. Experience evaluating emerging technologies, leading proofs of concept, and guiding their adoption based on business value, technical feasibility, risk, and operational readiness. Experience leading globally distributed engineering teams and collaborating across Product, Architecture, Security, SRE, Infrastructure, QA, and Operations. Strong understanding of distributed computing, cloud-native architecture, microservices, event-driven systems, and multi-region deployments. Experience building or operating large-scale, mission-critical systems on Google Cloud using technologies such as GKE, Cloud Run, Pub/Sub, Cloud Spanner, Cloud SQL, Bigtable, or Dataflow. Demonstrated success delivering secure, resilient, and high-quality solutions in a fast-paced environment with competing priorities and complex dependencies. Experience applying CI/CD, DevOps, infrastructure-as-code, observability, SRE, capacity planning, incident management, and disaster-recovery practices. Ability to communicate technical strategy, architecture decisions, delivery risks, and recommendations clearly to engineering teams, cross-functional partners, and senior leadership. Experience integrating emerging technologies, including AI-assisted engineering capabilities, into software development practices while maintaining appropriate security, quality, and governance controls. Information for US Applicants For roles located in the US, the estimated salary range for this position is $192,100.00 to $ 307,800.00 USD per year, which may include potential sales incentive payments (if applicable). Salary may vary depending on job-related factors which may include knowledge, skills, experience, and location. In addition, this position may be eligible for bonus and equity.Visa has a comprehensive benefits package for which this position may be eligible that includes Medical, Dental, Vision, 401(k), FSA/HSA, Life Insurance, Paid Time Off, and Wellness Program. Work Hours Varies upon the needs of the department. Travel Requirements This position requires travel 5-10% of the time. Mental/Physical Requirements This position will be performed in an office setting. The position will require the incumbent to sit and stand at a desk, communicate in person and by telephone, frequently operate standard office equipment, such as telephones and computers. Visa is an EEO Employer Qualified applicants will receive consideration for employment without regard to race, color religion, sex, national origin, sexual orientation, gender identity, disability or protect veteran status. Visa will also consider for employment qualified applicants with criminal histories in a manner consistent with the EEOC guidelines and applicable local law.
09/24/2026
Full time
About Us Visa is a world leader in payments technology, facilitating transactions between consumers, merchants, financial institutions and government entities across more than 200 countries and territories, dedicated to uplifting everyone, everywhere by being the best way to pay and be paid. At Visa, you'll have the opportunity to create impact at scale - tackling meaningful challenges, growing your skills and seeing your contributions impact lives around the world. Join Visa and do work that matters - to you, to your community, and to the world. Progress starts with you. Job Description Role Summary: The Sr. Manager; Software Engineering leads a team responsible for reviewing business requirements and developing functional and technical design documentation. This role oversees the design and implementation of testing procedures for APIs, abstractions, and integration patterns to address distributed computing challenges, including end-to-end and integration testing. The Sr. Manager participates in design reviews, providing input on requirements, product designs, schedules, and potential issues, and works across the team to ensure productivity, predictability, and delivery of high-quality results. The position includes accountability for code review and the team's development and testing activities, as well as participation in proof of concepts and technical evaluations of new technologies. The Sr. Manager is expected to foster a collaborative environment, ensuring the team meets business needs and delivers robust solutions. All roles require digital fluency, including the ability to work with emerging technologies such as Generative AI tools (e.g. ChatGPT, Microsoft Copilot) and agentic development to support everyday work. Essential Functions: People Leadership: Direct, mentor, and build a high-performing team of 8+ software engineers. Drive career development, performance management, regular 1:1s, and goal setting. Team Execution & Delivery: Oversee end-to-end application development deliverables including unit design, coding, code reviews, testing, and release management. Resource & Capacity Planning: Manage team workload, sprint planning, hiring, onboarding, and resource allocation to meet project timelines and deliverables. Technical & Strategic Guidance: Collaborate with technical leads, architects, and product managers to define solution approaches, assess scope, and evaluate technical feasibility. Agile Leadership: Drive agile development workflows utilizing modern open-source, CI/CD, and DevOps tools to streamline continuous integration and delivery pipelines. Cross-Functional Collaboration: Partner closely with global cross-functional teams, including product management, QA, security, and operations, to resolve blockers and ensure aligned delivery. Compliance & Quality: Ensure development adheres to Visa Development Management Methodology, Technical Security Requirements, and Secure Software Development Lifecycle (SSDLC) protocols. Documentation & Process Improvement: Maintain clear software, operational, and architectural documentation while continually improving engineering practices and procedures. Visa requires at least 3 days in office, expectations of these days will be confirmed by your Hiring Manager. Qualifications Basic Qualifications: 8+ years of relevant work experience and a Bachelors degree, OR 11+ years of relevant work experience. Experience building and supporting large-scale, high-performance, mission-critical applications. Strong hands-on software engineering experience, including coding, code reviews, and technical design. Proven ability to lead technical initiatives and drive architecture decisions. Experience with cloud-native technologies, distributed systems, APIs, and microservices. Experience supporting Tier-0 or highly available production systems. Ability to mentor engineers and provide technical leadership across engineering teams. Strong understanding of software engineering best practices, including CI/CD, testing, observability, and operational excellence Preferred Qualifications: 9 or more years of relevant work experience with a Bachelor Degree or 7 or more relevant years of experience with an Advanced Degree (e.g. Masters, MBA, JD, MD) or 3 or more years of experience with a PhD. 3+ years of direct people-management experience preferred; candidates who have led teams through technical influence, matrix leadership, or technical leadership without direct authority will also be considered Demonstrated experience leading software engineering teams, including delivery management, technical oversight, performance accountability, and talent development. Experience translating business and product requirements into scalable technical solutions, architecture decisions, delivery plans, and technical documentation. Proven ability to lead the design, development, testing, integration, deployment, and production support of complex, mission-critical applications. Strong experience overseeing API platforms, distributed systems, microservices, and enterprise integration patterns. Experience leading architecture and design reviews, evaluating technical tradeoffs, and ensuring solutions meet requirements for scalability, reliability, security, and performance. Experience establishing engineering standards and maintaining accountability for code quality, testing, secure development, release readiness, and production stability. Proven ability to manage delivery risks, dependencies, capacity, and priorities across multiple teams and cross-functional stakeholders. Experience evaluating emerging technologies, leading proofs of concept, and guiding their adoption based on business value, technical feasibility, risk, and operational readiness. Experience leading globally distributed engineering teams and collaborating across Product, Architecture, Security, SRE, Infrastructure, QA, and Operations. Strong understanding of distributed computing, cloud-native architecture, microservices, event-driven systems, and multi-region deployments. Experience building or operating large-scale, mission-critical systems on Google Cloud using technologies such as GKE, Cloud Run, Pub/Sub, Cloud Spanner, Cloud SQL, Bigtable, or Dataflow. Demonstrated success delivering secure, resilient, and high-quality solutions in a fast-paced environment with competing priorities and complex dependencies. Experience applying CI/CD, DevOps, infrastructure-as-code, observability, SRE, capacity planning, incident management, and disaster-recovery practices. Ability to communicate technical strategy, architecture decisions, delivery risks, and recommendations clearly to engineering teams, cross-functional partners, and senior leadership. Experience integrating emerging technologies, including AI-assisted engineering capabilities, into software development practices while maintaining appropriate security, quality, and governance controls. Information for US Applicants For roles located in the US, the estimated salary range for this position is $192,100.00 to $ 307,800.00 USD per year, which may include potential sales incentive payments (if applicable). Salary may vary depending on job-related factors which may include knowledge, skills, experience, and location. In addition, this position may be eligible for bonus and equity.Visa has a comprehensive benefits package for which this position may be eligible that includes Medical, Dental, Vision, 401(k), FSA/HSA, Life Insurance, Paid Time Off, and Wellness Program. Work Hours Varies upon the needs of the department. Travel Requirements This position requires travel 5-10% of the time. Mental/Physical Requirements This position will be performed in an office setting. The position will require the incumbent to sit and stand at a desk, communicate in person and by telephone, frequently operate standard office equipment, such as telephones and computers. Visa is an EEO Employer Qualified applicants will receive consideration for employment without regard to race, color religion, sex, national origin, sexual orientation, gender identity, disability or protect veteran status. Visa will also consider for employment qualified applicants with criminal histories in a manner consistent with the EEOC guidelines and applicable local law.
Job DescriptionJob DescriptionJob Title: Artificial Intelligence Senior Associate Location: Dearborn, MI (local preferred) Duration: 12 Months (with potential for extension) Interview: Interview onsite in South Lyon/Novi, MI Candidate must be USC or GC. Do not apply for C2C. It will be a hybrid position. REVIEW JD MAKE SURE REQUIRED SKILLS (highlighted red) ARE ON THE RESUME. Will be onsite 4 days a week. Position Description: Employees in this job function are responsible for developing intelligent programs, cognitive applications and algorithms for data analysis and automation, leveraging various AI techniques such as deep learning, generative AI, natural language processing, image processing, cognitive automation, intelligent process automation, reinforcement learning, virtual assistants and specialized programming Key Responsibilities: Understand business requirements and develop AI algorithms, models and programs to solve complex problems, generate recommendations, extract patterns, make predictions, interpret sensor data (images, sound), orchestrate automation and enable self-service capabilities Perform large-scale experimentation and develop data driven applications that translate data into actionable intelligence Drive innovative applications of Artificial Intelligence tools and techniques such as deep learning, generative AI, natural language processing, image processing, cognitive automation, intelligent process automation, reinforcement learning, virtual assistants and specialized programming Research and optimize AI technologies to enhance efficiency and accuracy of data analysis and create more efficient automation Skills Required: Google Cloud Platform Experience Required: Bachelor's or Master's degree in Computer Science, Software Engineering, or related field (or equivalent practical experience). 3+ years building production software systems, including 1-2+ years on ML/AI or LLM-based applications. Proven experience designing and deploying multi-agent or multi-service architectures in production - not just notebooks or demos. As one 2026 hiring analysis puts it, the job is closer to distributed systems engineering with a probabilistic component than it is to ML research or prompt tweaking . Strong Python proficiency, including async/concurrent programming, and experience with backend frameworks (FastAPI, Flask). Hands-on experience with agent orchestration frameworks - LangGraph, CrewAI, LlamaIndex, or equivalent - for building stateful, multi-step, tool-using agent workflows. Practical experience building RAG pipelines: vector databases (pgvector, Pinecone, Weaviate, or Qdrant), embeddings, chunking strategies, and retrieval evaluation. Cloud deployment experience, ideally Google Cloud Platform (BigQuery, Cloud Run/GKE, Vertex AI, Pub/Sub) or equivalent AWS/Azure services. Strong SQL skills and experience with cloud data warehouses. Containerization and CI/CD experience (Docker, Kubernetes, GitHub Actions/Cloud Build). Experience building evaluation and observability pipelines for LLM/agent systems - offline eval sets, LLM-as-judge scoring, and tracing tools (LangSmith, Langfuse, OpenTelemetry, or equivalent) to track task success, latency, and cost. Understanding of LLM safety practices: guardrails, output validation, prompt-injection defense, and safe execution of AI-generated code/SQL (sandboxing, least privilege). Solid software engineering fundamentals: API design, testing, version control, security best practices. Experience Preferred: Experience with cost optimization and model routing - designing tiered pipelines that route between low-cost and high-capability models based on task complexity, and modeling per-conversation or per-task cost at scale. Experience deploying agentic systems with human-in-the-loop or multi-checkpoint validation workflows for high-reliability/high-stakes use cases. Experience with automotive, EV charging, IoT, or connected-vehicle telemetry data. Familiarity with Model Context Protocol (MCP) or similar standards for tool/data integration across agents. Prior experience in a startup or 0-to-1 product environment, comfortable with ambiguity and fast-evolving requirements. Education Required: Bachelor's Degree Education Preferred: Master's Degree Additional Information: Architect and deploy the production multi-agent orchestration layer (interpreter/orchestrator, NL-to-SQL agent, visualization agent, RCA/RAG agent, report composition agent, notification agent), using modern agent frameworks with state management and checkpointing rather than ad-hoc loops. Design and productionize RAG pipelines (chunking, embeddings, hybrid retrieval, reranking) grounded in approved schemas, engineering documentation, and historical issue records. Own BigQuery integration and enforce safe, least-privilege, validated execution of LLM-generated SQL. Build CI/CD, containerization, and infrastructure-as-code for deploying agent services on GCP (Cloud Run/GKE, Vertex AI). Implement evaluation pipelines and observability/tracing for every agent (golden datasets, LLM-as-judge scoring, regression alerts) so quality is measurable, not assumed. Implement guardrails, prompt-injection defenses, and human-in-the-loop approval checkpoints to ensure correctness and safety before any output triggers downstream action. Design cost/latency optimization strategies, including tiered model routing (cheap filter models vs. high-capability deep-dive models) and caching. Integrate validated outputs with operational systems (Salesforce ticketing, driver/site-manager notifications) and report export pipelines (PDF/HTML/spreadsheet). Collaborate with data scientists to productionize prototypes (anomaly detection, diagnostic agents) into scalable, monitored services. Establish versioning, testing, and safe rollout practices (canary/shadow deployments) for evolving agent logic.
09/23/2026
Full time
Job DescriptionJob DescriptionJob Title: Artificial Intelligence Senior Associate Location: Dearborn, MI (local preferred) Duration: 12 Months (with potential for extension) Interview: Interview onsite in South Lyon/Novi, MI Candidate must be USC or GC. Do not apply for C2C. It will be a hybrid position. REVIEW JD MAKE SURE REQUIRED SKILLS (highlighted red) ARE ON THE RESUME. Will be onsite 4 days a week. Position Description: Employees in this job function are responsible for developing intelligent programs, cognitive applications and algorithms for data analysis and automation, leveraging various AI techniques such as deep learning, generative AI, natural language processing, image processing, cognitive automation, intelligent process automation, reinforcement learning, virtual assistants and specialized programming Key Responsibilities: Understand business requirements and develop AI algorithms, models and programs to solve complex problems, generate recommendations, extract patterns, make predictions, interpret sensor data (images, sound), orchestrate automation and enable self-service capabilities Perform large-scale experimentation and develop data driven applications that translate data into actionable intelligence Drive innovative applications of Artificial Intelligence tools and techniques such as deep learning, generative AI, natural language processing, image processing, cognitive automation, intelligent process automation, reinforcement learning, virtual assistants and specialized programming Research and optimize AI technologies to enhance efficiency and accuracy of data analysis and create more efficient automation Skills Required: Google Cloud Platform Experience Required: Bachelor's or Master's degree in Computer Science, Software Engineering, or related field (or equivalent practical experience). 3+ years building production software systems, including 1-2+ years on ML/AI or LLM-based applications. Proven experience designing and deploying multi-agent or multi-service architectures in production - not just notebooks or demos. As one 2026 hiring analysis puts it, the job is closer to distributed systems engineering with a probabilistic component than it is to ML research or prompt tweaking . Strong Python proficiency, including async/concurrent programming, and experience with backend frameworks (FastAPI, Flask). Hands-on experience with agent orchestration frameworks - LangGraph, CrewAI, LlamaIndex, or equivalent - for building stateful, multi-step, tool-using agent workflows. Practical experience building RAG pipelines: vector databases (pgvector, Pinecone, Weaviate, or Qdrant), embeddings, chunking strategies, and retrieval evaluation. Cloud deployment experience, ideally Google Cloud Platform (BigQuery, Cloud Run/GKE, Vertex AI, Pub/Sub) or equivalent AWS/Azure services. Strong SQL skills and experience with cloud data warehouses. Containerization and CI/CD experience (Docker, Kubernetes, GitHub Actions/Cloud Build). Experience building evaluation and observability pipelines for LLM/agent systems - offline eval sets, LLM-as-judge scoring, and tracing tools (LangSmith, Langfuse, OpenTelemetry, or equivalent) to track task success, latency, and cost. Understanding of LLM safety practices: guardrails, output validation, prompt-injection defense, and safe execution of AI-generated code/SQL (sandboxing, least privilege). Solid software engineering fundamentals: API design, testing, version control, security best practices. Experience Preferred: Experience with cost optimization and model routing - designing tiered pipelines that route between low-cost and high-capability models based on task complexity, and modeling per-conversation or per-task cost at scale. Experience deploying agentic systems with human-in-the-loop or multi-checkpoint validation workflows for high-reliability/high-stakes use cases. Experience with automotive, EV charging, IoT, or connected-vehicle telemetry data. Familiarity with Model Context Protocol (MCP) or similar standards for tool/data integration across agents. Prior experience in a startup or 0-to-1 product environment, comfortable with ambiguity and fast-evolving requirements. Education Required: Bachelor's Degree Education Preferred: Master's Degree Additional Information: Architect and deploy the production multi-agent orchestration layer (interpreter/orchestrator, NL-to-SQL agent, visualization agent, RCA/RAG agent, report composition agent, notification agent), using modern agent frameworks with state management and checkpointing rather than ad-hoc loops. Design and productionize RAG pipelines (chunking, embeddings, hybrid retrieval, reranking) grounded in approved schemas, engineering documentation, and historical issue records. Own BigQuery integration and enforce safe, least-privilege, validated execution of LLM-generated SQL. Build CI/CD, containerization, and infrastructure-as-code for deploying agent services on GCP (Cloud Run/GKE, Vertex AI). Implement evaluation pipelines and observability/tracing for every agent (golden datasets, LLM-as-judge scoring, regression alerts) so quality is measurable, not assumed. Implement guardrails, prompt-injection defenses, and human-in-the-loop approval checkpoints to ensure correctness and safety before any output triggers downstream action. Design cost/latency optimization strategies, including tiered model routing (cheap filter models vs. high-capability deep-dive models) and caching. Integrate validated outputs with operational systems (Salesforce ticketing, driver/site-manager notifications) and report export pipelines (PDF/HTML/spreadsheet). Collaborate with data scientists to productionize prototypes (anomaly detection, diagnostic agents) into scalable, monitored services. Establish versioning, testing, and safe rollout practices (canary/shadow deployments) for evolving agent logic.
Job DescriptionJob DescriptionJob Title: Artificial Intelligence Senior Associate Location: Dearborn, MI (local preferred) Duration: 12 Months (with potential for extension) Interview: Interview onsite in South Lyon/Novi, MI Candidate must be USC or GC. Do not apply for C2C. It will be a hybrid position. REVIEW JD MAKE SURE REQUIRED SKILLS (highlighted red) ARE ON THE RESUME. Will be onsite 4 days a week. Position Description: Employees in this job function are responsible for developing intelligent programs, cognitive applications and algorithms for data analysis and automation, leveraging various AI techniques such as deep learning, generative AI, natural language processing, image processing, cognitive automation, intelligent process automation, reinforcement learning, virtual assistants and specialized programming Key Responsibilities: Understand business requirements and develop AI algorithms, models and programs to solve complex problems, generate recommendations, extract patterns, make predictions, interpret sensor data (images, sound), orchestrate automation and enable self-service capabilities Perform large-scale experimentation and develop data driven applications that translate data into actionable intelligence Drive innovative applications of Artificial Intelligence tools and techniques such as deep learning, generative AI, natural language processing, image processing, cognitive automation, intelligent process automation, reinforcement learning, virtual assistants and specialized programming Research and optimize AI technologies to enhance efficiency and accuracy of data analysis and create more efficient automation Skills Required: Google Cloud Platform Experience Required: Bachelor's or Master's degree in Computer Science, Software Engineering, or related field (or equivalent practical experience). 3+ years building production software systems, including 1-2+ years on ML/AI or LLM-based applications. Proven experience designing and deploying multi-agent or multi-service architectures in production - not just notebooks or demos. As one 2026 hiring analysis puts it, the job is closer to distributed systems engineering with a probabilistic component than it is to ML research or prompt tweaking . Strong Python proficiency, including async/concurrent programming, and experience with backend frameworks (FastAPI, Flask). Hands-on experience with agent orchestration frameworks - LangGraph, CrewAI, LlamaIndex, or equivalent - for building stateful, multi-step, tool-using agent workflows. Practical experience building RAG pipelines: vector databases (pgvector, Pinecone, Weaviate, or Qdrant), embeddings, chunking strategies, and retrieval evaluation. Cloud deployment experience, ideally Google Cloud Platform (BigQuery, Cloud Run/GKE, Vertex AI, Pub/Sub) or equivalent AWS/Azure services. Strong SQL skills and experience with cloud data warehouses. Containerization and CI/CD experience (Docker, Kubernetes, GitHub Actions/Cloud Build). Experience building evaluation and observability pipelines for LLM/agent systems - offline eval sets, LLM-as-judge scoring, and tracing tools (LangSmith, Langfuse, OpenTelemetry, or equivalent) to track task success, latency, and cost. Understanding of LLM safety practices: guardrails, output validation, prompt-injection defense, and safe execution of AI-generated code/SQL (sandboxing, least privilege). Solid software engineering fundamentals: API design, testing, version control, security best practices. Experience Preferred: Experience with cost optimization and model routing - designing tiered pipelines that route between low-cost and high-capability models based on task complexity, and modeling per-conversation or per-task cost at scale. Experience deploying agentic systems with human-in-the-loop or multi-checkpoint validation workflows for high-reliability/high-stakes use cases. Experience with automotive, EV charging, IoT, or connected-vehicle telemetry data. Familiarity with Model Context Protocol (MCP) or similar standards for tool/data integration across agents. Prior experience in a startup or 0-to-1 product environment, comfortable with ambiguity and fast-evolving requirements. Education Required: Bachelor's Degree Education Preferred: Master's Degree Additional Information: Architect and deploy the production multi-agent orchestration layer (interpreter/orchestrator, NL-to-SQL agent, visualization agent, RCA/RAG agent, report composition agent, notification agent), using modern agent frameworks with state management and checkpointing rather than ad-hoc loops. Design and productionize RAG pipelines (chunking, embeddings, hybrid retrieval, reranking) grounded in approved schemas, engineering documentation, and historical issue records. Own BigQuery integration and enforce safe, least-privilege, validated execution of LLM-generated SQL. Build CI/CD, containerization, and infrastructure-as-code for deploying agent services on GCP (Cloud Run/GKE, Vertex AI). Implement evaluation pipelines and observability/tracing for every agent (golden datasets, LLM-as-judge scoring, regression alerts) so quality is measurable, not assumed. Implement guardrails, prompt-injection defenses, and human-in-the-loop approval checkpoints to ensure correctness and safety before any output triggers downstream action. Design cost/latency optimization strategies, including tiered model routing (cheap filter models vs. high-capability deep-dive models) and caching. Integrate validated outputs with operational systems (Salesforce ticketing, driver/site-manager notifications) and report export pipelines (PDF/HTML/spreadsheet). Collaborate with data scientists to productionize prototypes (anomaly detection, diagnostic agents) into scalable, monitored services. Establish versioning, testing, and safe rollout practices (canary/shadow deployments) for evolving agent logic.
09/23/2026
Full time
Job DescriptionJob DescriptionJob Title: Artificial Intelligence Senior Associate Location: Dearborn, MI (local preferred) Duration: 12 Months (with potential for extension) Interview: Interview onsite in South Lyon/Novi, MI Candidate must be USC or GC. Do not apply for C2C. It will be a hybrid position. REVIEW JD MAKE SURE REQUIRED SKILLS (highlighted red) ARE ON THE RESUME. Will be onsite 4 days a week. Position Description: Employees in this job function are responsible for developing intelligent programs, cognitive applications and algorithms for data analysis and automation, leveraging various AI techniques such as deep learning, generative AI, natural language processing, image processing, cognitive automation, intelligent process automation, reinforcement learning, virtual assistants and specialized programming Key Responsibilities: Understand business requirements and develop AI algorithms, models and programs to solve complex problems, generate recommendations, extract patterns, make predictions, interpret sensor data (images, sound), orchestrate automation and enable self-service capabilities Perform large-scale experimentation and develop data driven applications that translate data into actionable intelligence Drive innovative applications of Artificial Intelligence tools and techniques such as deep learning, generative AI, natural language processing, image processing, cognitive automation, intelligent process automation, reinforcement learning, virtual assistants and specialized programming Research and optimize AI technologies to enhance efficiency and accuracy of data analysis and create more efficient automation Skills Required: Google Cloud Platform Experience Required: Bachelor's or Master's degree in Computer Science, Software Engineering, or related field (or equivalent practical experience). 3+ years building production software systems, including 1-2+ years on ML/AI or LLM-based applications. Proven experience designing and deploying multi-agent or multi-service architectures in production - not just notebooks or demos. As one 2026 hiring analysis puts it, the job is closer to distributed systems engineering with a probabilistic component than it is to ML research or prompt tweaking . Strong Python proficiency, including async/concurrent programming, and experience with backend frameworks (FastAPI, Flask). Hands-on experience with agent orchestration frameworks - LangGraph, CrewAI, LlamaIndex, or equivalent - for building stateful, multi-step, tool-using agent workflows. Practical experience building RAG pipelines: vector databases (pgvector, Pinecone, Weaviate, or Qdrant), embeddings, chunking strategies, and retrieval evaluation. Cloud deployment experience, ideally Google Cloud Platform (BigQuery, Cloud Run/GKE, Vertex AI, Pub/Sub) or equivalent AWS/Azure services. Strong SQL skills and experience with cloud data warehouses. Containerization and CI/CD experience (Docker, Kubernetes, GitHub Actions/Cloud Build). Experience building evaluation and observability pipelines for LLM/agent systems - offline eval sets, LLM-as-judge scoring, and tracing tools (LangSmith, Langfuse, OpenTelemetry, or equivalent) to track task success, latency, and cost. Understanding of LLM safety practices: guardrails, output validation, prompt-injection defense, and safe execution of AI-generated code/SQL (sandboxing, least privilege). Solid software engineering fundamentals: API design, testing, version control, security best practices. Experience Preferred: Experience with cost optimization and model routing - designing tiered pipelines that route between low-cost and high-capability models based on task complexity, and modeling per-conversation or per-task cost at scale. Experience deploying agentic systems with human-in-the-loop or multi-checkpoint validation workflows for high-reliability/high-stakes use cases. Experience with automotive, EV charging, IoT, or connected-vehicle telemetry data. Familiarity with Model Context Protocol (MCP) or similar standards for tool/data integration across agents. Prior experience in a startup or 0-to-1 product environment, comfortable with ambiguity and fast-evolving requirements. Education Required: Bachelor's Degree Education Preferred: Master's Degree Additional Information: Architect and deploy the production multi-agent orchestration layer (interpreter/orchestrator, NL-to-SQL agent, visualization agent, RCA/RAG agent, report composition agent, notification agent), using modern agent frameworks with state management and checkpointing rather than ad-hoc loops. Design and productionize RAG pipelines (chunking, embeddings, hybrid retrieval, reranking) grounded in approved schemas, engineering documentation, and historical issue records. Own BigQuery integration and enforce safe, least-privilege, validated execution of LLM-generated SQL. Build CI/CD, containerization, and infrastructure-as-code for deploying agent services on GCP (Cloud Run/GKE, Vertex AI). Implement evaluation pipelines and observability/tracing for every agent (golden datasets, LLM-as-judge scoring, regression alerts) so quality is measurable, not assumed. Implement guardrails, prompt-injection defenses, and human-in-the-loop approval checkpoints to ensure correctness and safety before any output triggers downstream action. Design cost/latency optimization strategies, including tiered model routing (cheap filter models vs. high-capability deep-dive models) and caching. Integrate validated outputs with operational systems (Salesforce ticketing, driver/site-manager notifications) and report export pipelines (PDF/HTML/spreadsheet). Collaborate with data scientists to productionize prototypes (anomaly detection, diagnostic agents) into scalable, monitored services. Establish versioning, testing, and safe rollout practices (canary/shadow deployments) for evolving agent logic.