Senior Manager AI/ML Engineering

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:

• Generative AI LLM Development
  • Design and implement GenAI solutions using OpenAI compatible APIs, AWS, Codex and Claude
  • Fine tune and adapt LLMs using enterprise data where applicable
  • Develop robust prompt engineering frameworks
  • Evaluate responses for accuracy, relevance, and safety
  • RAG Knowledge Grounding
  • Build Retrieval Augmented Generation (RAG) pipelines
  • Integrate vector databases, embeddings, and semantic search
  • Leverage Azure/AWS/Google Document Intelligence and OCR for document ingestion
  • Reduce hallucination through knowledge grounding and retrieval strategies
• Agentic AI Intelligent Systems
  • Design Agentic AI architectures capable of reasoning and planning
  • Build autonomous agents that interact with tools, APIs, and workflows using AIDLC
  • Orchestrate multi agent systems for complex enterprise tasks
• Machine Learning Advanced Analytics
  • Design, develop, and deploy ML models to analyze enterprise data
  • Build time series and forecasting models to predict trends, demand, and outcomes
  • Implement anomaly detection frameworks to surface abnormal patterns
  • Perform correlation and root cause analysis across multi source datasets
  • Apply supervised, unsupervised, and semi supervised learning techniques
• Data ML Engineering
  • Develop scalable ALML pipelines using Python and Databricks
  • Perform feature engineering on large, complex enterprise datasets
  • Partner with data engineers to ensure data quality, availability, and observability
  • Deploy models into production environments and setup AIML Ops with monitoring and alerting capabilities
• Architecture Delivery Leadership
  • Own the technical design and architecture of AIML solutions for claims automation and enterprise chatbot platforms
  • Translate CX business problems into ML problem statements and metrics
  • Drive end to end delivery from experimentation to production rollout across product, UX, and engineering teams
  • Mentor junior engineers and contribute to internal ML best practices
  • Integrate AI capabilities into frontend (React/Angular) and backend (Java/.NET) systems
  • Ensure production readiness with monitoring, security, and cost controls
• Responsible Explainable AI
  • Ensure models are interpretable, stable, and trustworthy
  • Implement guardrails, safety filters, and human in the loop patterns
  • Ensure compliance with enterprise security and AI governance standards
  • Incorporate fairness, transparency, and governance standards
  • Monitor model drift and behavior, latency, and cost in production
• Behavioral Expectations
  • Business first, outcome driven problem solver
  • Product oriented mindset with focus on usability and adoption
  • Solid ownership mindset with accountability for production outcomes
  • Analytical, hypothesis driven approach to problem solving
  • Ability to explain complex ML insights to non technical stakeholders
  • Systems thinker with end to end awareness of data and decision flows
  • Mentor and technical leader within the ML community
  • Architecture led decision making for scalable GenAI systems
  • Solid ownership of AI safety, risk, and governance
  • Collaborative and cross functional working style
  • Experimental yet disciplined approach to innovation
  • Continuous learning in rapidly evolving GenAI ecosystems
• Advanced Technologies AI Techniques
  • LLMs: Azure OpenAI, Claude, OpenAI compatible APIs, Codex, Gemini, AWS
  • Prompt Engineering Evaluation Frameworks
  • RAG Architecture: Vector databases, embeddings, semantic search
  • Agentic AI: Autonomous agents, tool augmented LLMs, multi agent orchestration
  • OCR Document Processing: Azure Document Intelligence
  • Platforms: AWS, LLM Gateway, Azure ML, Azure Functions
  • Observability: Azure Monitor, Application Insights, Response monitoring, latency tracking…