Lead AI or ML Engineer

Optum is a global organization that delivers care, aided by technology to help millions of people live healthier lives. The work you do with our team will directly improve health outcomes by connecting people with the care, pharmacy benefits, data and resources they need to feel their best. Here, you will find a culture guided by inclusion, talented peers, comprehensive benefits and career development opportunities. Come make an impact on the communities we serve as you help us advance health optimization on a global scale. Join us to start Caring. Connecting. Growing together.

Primary Responsibilities:

• AI Architecture & Solution Design:
  • Design enterprise-grade AI/ML and Generative AI architectures aligned with business and technology strategies
  • Define scalable patterns for:
    • LLM integration
    • RAG (Retrieval-Augmented Generation)
    • Agentic AI workflows
    • AI orchestration frameworks
    • Vector databases
    • AI gateways
    • Model serving and inference
  • Architect multi-cloud AI solutions across Azure, AWS, and GCP
  • Define AI platform standards, reusable components, and reference architectures
• Hands-on Engineering:
  • Build production-ready AI services and APIs using Python, Java, or Node.js.
  • Develop and optimize:
    • Prompt engineering frameworks
    • AI agents
    • Semantic search pipelines
    • Embedding workflows
    • Fine-tuning pipelines
  • Implement integrations with enterprise systems using APIs, event-driven architectures, and streaming technologies
  • Work directly with engineering teams on coding, troubleshooting, performance tuning, and deployment automation
• Generative AI & LLM Engineering:
  • Evaluate and implement enterprise LLM solutions including OpenAI, Anthropic, Gemini, and open-source models
  • Design secure and compliant GenAI architectures with guardrails, governance, and observability
  • Implement:
    • RAG pipelines
    • Vector search
    • Knowledge grounding
    • AI evaluation frameworks
    • Hallucination mitigation strategies
  • Establish responsible AI practices including explainability, privacy, and bias mitigation
• Cloud & Platform Engineering:
  • Design AI infrastructure on Azure OpenAI, AWS Bedrock, Vertex AI, Kubernetes, and container platforms
  • Build CI/CD pipelines for ML and AI workloads
  • Implement MLOps and LLMOps practices for model lifecycle management
  • Drive infrastructure automation using Terraform, Helm, GitHub Actions, or Azure DevOps
• Data & Integration:
  • Collaborate with data engineering teams to build scalable AI data pipelines
  • Design event-driven and streaming architectures using Kafka and modern integration frameworks
  • Ensure interoperability and API-first design principles across platforms
• Governance & Leadership:
  • Define enterprise AI standards, policies, and architecture governance
  • Mentor engineers and architects on AI best practices
  • Conduct architecture reviews and technology evaluations
  • Partner with security and compliance teams to ensure adherence to enterprise standards
• Comply with the terms and conditions of the employment contract, company policies and procedures, and any and all directives (such as, but not limited to, transfer and/or re-assignment to different work locations, change in teams and/or work shifts, policies in regards to flexibility of work benefits and/or work environment, alternative work arrangements, and other decisions that may arise due to the changing business environment). The Company may adopt, vary or rescind these policies and directives in its absolute discretion and without any limitation (implied or otherwise) on its ability to do so

You’ll be rewarded and recognized for your performance in an environment that will challenge you and give you clear direction on what it takes to succeed in your role as well as provide development for other roles you may be interested in.

Required Qualifications:

• Bachelor’s or Master’s degree in Computer Science, Engineering, AI, Data Science, or related field
• 10+ years of software engineering/architecture experience
• Hands-on experience with AI/ML or Generative AI implementation
• Experience deploying enterprise solutions in production environments
• Solid expertise in:
  • Python
  • APIs & microservices
  • Cloud-native architectures
  • Kubernetes & containers
  • AI/ML frameworks

Preferred Qualifications:

• AI/Cloud certifications
• Experience in healthcare, finance,…