Lead EngineerAI Application Engineering

Job Title: Lead Engineer-AI Application Engineering
Location: Remote, but interviews or laptop pickup must occur at one of the following locations: San Francisco, Arlington, VA, Denver, CO, Chicago, Boston, NYC, Houston, Miami, Los Angeles, Seattle, Dallas, Atlanta, GA, Minneapolis, Minn, Birmingham, MI or Irvine, CA

Length: 6-10 months

 
Lead Engineer- AI Application Engineering
Most important requirement. The candidate should design scalable, secure, high-performance systems, make architecture decisions, own code quality, and guide technical direction.

Productionization of AI Use Cases
Take AI PoCs/MVPs to production by building scaffolding, APIs, services, integrations, pipelines, environments, and release processes.

AI Application Engineering, Not Deep ML
No deep model-building required. Need practical experience with RAG, agentic workflows, LLM/API integrations, evaluation, observability, and partnering with AI Engineers.

Azure + DevOps Delivery
Strong Azure and CI/CD experience across infrastructure, security, identity, networking, environment management, and deployment readiness.

Enterprise Integration and Security
Integrate AI applications with UAIS, Optum, UHG, and enterprise systems while meeting security, compliance, and enterprise standards.

Hands-On Builder Mindset
Not a strategy-only architect. Must be able to code, review code, build services, set up repos/pipelines, and improve engineering practices.

 
Software Engineer -
The person should have experience introducing these software engineering best practices (architecture/design patterns, building scalable, high performant and secure solutions) to AI engineering teams.

Job Summary
We are seeking a Software Engineer to build and deploy AI-enabled applications in partnership with AI Engineers and product teams. This role focuses on application architecture, project scaffolding, enterprise integration, cloud implementation, and deployment readiness needed to move AI use cases from PoC to MVP and production.
The ideal candidate brings strong software engineering and cloud delivery experience, with practical exposure to AI solutions and common AI architecture patterns. This person does not need deep AI model-development expertise, but should understand how AI applications are structured, collaborate effectively with AI Engineers, and be adept at using modern AI productivity tools such as GitHub Copilot, Claude Code, and similar tools in a disciplined way to accelerate engineering delivery.

Key Responsibilities
• Architect and build high performance, scalable and secure AI solutions.
• Introduce and implement software engineering best practices (architecture/design patterns, building scalable, high performant and secure solutions).
• Responsible for code reviews and scalability and security of production deployed systems
• Integrate AI solutions with UAIS and other UHG/Optum systems to enable secure enterprise deployment.
• Select and apply the right technical patterns for AI solutions in partnership with AI Engineers.
• Scaffold projects, repositories, pipelines, environments, and shared services needed for delivery.
• Build core application components, APIs, data integrations, and deployment-ready services.
• Partner closely with DevOps and platform teams to ensure secure, scalable, and supportable deployments.
• Lead CI/CD, testing, release processes, and operational readiness for MVP and production solutions.

Skills & Experience
• Strong software engineering fundamentals with a hands-on builder mindset and experience delivering production-grade applications.
• Some who has experience with and introduced software engineering best practices (architecture/design patterns, building scalable, high performant and secure solutions) to AI engineering teams and AI solutions.
• Practical experience working on AI solutions alongside AI Engineers, with understanding of common patterns such as RAG, agentic workflows, API-based model integration, and evaluation or observability needs.
• Strong Azure experience across infrastructure, platform services, security, identity, and networking.
• Experience with DevOps tooling, CI/CD pipelines, environment management, and secure deployment practices.
• Strong experience with open source and cloud technologies such as Neo4j, Cosmos DB, PostgreSQL, MongoDB, Kafka, Containers, K8s, FastAPI, and related application frameworks.
• Strong understanding of data platform choices, including when to use relational, NoSQL, graph, vector, and event-driven architectural patterns.
• Proven ability to deliver across multiple Optum or UHG projects and work effectively within enterprise standards and constraints.
• Proficiency with GitHub workflows, branching strategies, pull requests, and modern AI productivity tools such as GitHub Copilot and Claude Code.