Senior AI/ML Engineer (Gen AI Specialist)
Optum (UnitedHealth Group) · Healthcare & Pharma
- Bangalore, Karnataka, India
- Hybrid
- Posted today
- Data & AI
About the job
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.
We're looking for a highly skilled Lead Generative AI & Agentic AI Architect to drive the design, development, and enterprise adoption of Large Language Model (LLM), Generative AI, and Agentic AI solutions. This role is responsible for defining enterprise AI architecture, establishing AI platform capabilities, developing domain-specific AI systems, and operationalizing AI solutions that deliver measurable business value at scale.
The ideal candidate combines deep expertise in foundation models, AI platforms, retrieval architectures, agentic frameworks, model optimization, and enterprise AI governance. You will partner with product, engineering, security, architecture, and business leaders to build scalable AI platforms, intelligent applications, AI agents, and reusable enterprise capabilities that accelerate AI adoption across the organization.
Primary Responsibilities:
Enterprise AI Architecture & Strategy - Lead the architecture, design, and implementation of enterprise-scale Generative AI and Agentic AI solutions aligned with business objectives
Define enterprise AI architecture standards, reference architectures, governance models, and platform capabilities supporting scalable AI adoption
Evaluate, benchmark, select, and operationalize proprietary and open-source foundation models based on reasoning capability, response quality, latency, scalability, security, and cost considerations
Design multi-model AI architectures and intelligent routing strategies that dynamically select optimal models based on workload requirements, performance characteristics, and operational efficiency
Establish reusable AI frameworks, accelerators, and architecture patterns that enable rapid enterprise adoption of Generative AI capabilities
Foundation Models & Model Engineering - Design, develop, fine-tune, and operationalize foundation models, custom language models, and domain-specific AI solutions
Apply advanced model adaptation techniques including: - Supervised Fine-Tuning (SFT)
LoRA
QLoRA
PEFT
Instruction Tuning
Domain Adaptation
Optimize model performance through inference tuning, quantization strategies, context management, and workload optimization
Manage model lifecycle processes including: - Model versioning
Validation and testing
Release management
Production readiness reviews
Governance and compliance controls
Generative AI & Retrieval Architecture - Architect and implement enterprise Retrieval-Augmented Generation (RAG) solutions supporting knowledge-intensive AI applications
Design end-to-end retrieval architectures including: - Document ingestion
Content processing and chunking
Embedding generation
Vector indexing
Semantic retrieval
Grounding strategies
Retrieval optimization
Build AI-ready knowledge platforms that enable accurate, context-aware, and explainable responses
Design semantic search, hybrid search, reranking, and vector retrieval frameworks to maximize retrieval quality and answer relevance
Establish knowledge retrieval architectures that support enterprise search, copilots, intelligent assistants, and decision-support systems
Agentic AI & Intelligent Automation - Design and develop Agentic AI systems capable of: - Autonomous reasoning
Planning and decision-making
Task execution
Workflow orchestration
Tool utilization
Build and operationalize single-agent and multi-agent architectures for enterprise use cases
Integrate AI agents with: - Enterprise applications
APIs
Databases
Knowledge repositories
Business workflows
External services
Design context engineering strategies including: - Agent memory management
Session persistence
Context orchestration
Retrieval coordination
Long-running conversation management
AI Platforms & Enterprise Integration - Build enterprise AI platforms that provide centralized access, orchestration, governance, and management across multiple AI model providers
Design AI gateway capabilities supporting: - Model routing
Provider abstraction
Policy enforcement
Access management
Usage governance
Cost controls
Leverage cloud-based AI platforms including: - Azure OpenAI
Azure AI Foundry
AWS Bedrock
Google Vertex AI
Comparable enterprise AI ecosystems
Design scalable AI services and APIs that enable secure integration across enterprise systems and applications
AI Quality, Evaluation & Observability - Implement end-to-end AI observability across agentic systems, retrieval frameworks, AI workflows, and foundation model deployments
Develop capabilities including: - Prompt tracing
Agent tracing
Workflow tracing
Retrieval tracing
Inference monitoring
Operational telemetry
Define and continuously improve AI quality metrics including: - Response accuracy
Groundedness
Hallucination rates
Latency
Throughput
Token consumption
Task completion rates
User satisfaction
Design automated evaluation frameworks leveraging: - Benchmark datasets
Human feedback
Synthetic evaluations
Regression testing
Production telemetry
Responsible AI, Governance & Cost Optimization - Establish Responsible AI standards, governance frameworks, and enterprise AI controls.
Implement: - Safety guardrails
Content filtering
Data protection controls
Prompt injection protection
Security policies
Compliance requirements
Drive AI cost optimization through: - Token utilization management
Prompt optimization
Context optimization
Caching strategies
Model routing
Inference optimization
Ensure AI systems meet enterprise requirements for scalability, security, reliability, compliance, and operational excellence
Leadership & Stakeholder Management - Collaborate with engineering, architecture, security, product, data, and business teams to define AI strategy, standards, roadmaps, and adoption plans
Provide technical leadership and guidance for enterprise AI initiatives and strategic programs
Mentor engineers, architects, and AI practitioners while promoting AI engineering best practices
Drive enterprise AI innovation through reusable frameworks, architecture standards, platform capabilities, and governance models
Collaborate with research, engineering, and product teams to translate cutting-edge AI advancements into production-ready capabilities. Uphold ethical AI principles by embedding fairness, transparency, and accountability throughout the model development lifecycle
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
Required Qualifications:
Bachelor's degree in computer science, Artificial Intelligence, Machine Learning, Data Science, Engineering, or a related field
8+ years of experience in Artificial Intelligence, Machine Learning, Data Science, Software Engineering, or AI Platform Engineering
Proven experience delivering enterprise-scale Generative AI, LLM, and Agentic AI solutions in production environments
Experience evaluating, selecting, fine-tuning, deploying, and managing open-source and commercial foundation models
Hands-on experience designing and implementing RAG architectures, semantic retrieval systems, and vector-based knowledge platforms
Experience building AI platforms, AI gateways, and enterprise AI services operating across multiple foundation model providers
Experience implementing AI observability, evaluation frameworks, performance monitoring, and operational governance
Experience with cloud-native AI platforms and managed AI services
Solid programming experience in Python and modern AI development frameworks
Solid understanding of: - Transformer architectures
Embeddings
Tokenization
Attention mechanisms
Context windows
Model performance characteristics
Inference optimization
Solid understanding of Responsible AI, model governance, safety controls, and enterprise compliance requirements
Proven solid expertise in Agentic AI architectures, orchestration frameworks, tool integration, and intelligent workflow automation
Proven excellent communication, technical leadership, stakeholder management, and problem-solving skills.
Preferred Qualifications:
Master's degree
Experience architecting enterprise AI platforms supporting Generative AI, Agentic AI, copilots, intelligent assistants, and AI-powered business applications
Hands-on experience with LLM orchestration frameworks such as LangChain, LangGraph, Semantic Kernel, Llama Index, AutoGen, CrewAI, or equivalent technologies
Experience designing advanced RAG architectures incorporating vector databases, semantic search, hybrid search, reranking, graph-based retrieval, and enterprise knowledge systems
Experience building multi-agent systems, agent memory architectures, planning frameworks, and autonomous workflow solutions
Experience with vector databases and knowledge platforms such as Pinecone, Weaviate, Chroma, FAISS, pgvector, Azure AI Search, Neo4j, or equivalent technologies
Experience implementing AI observability, tracing, evaluation, and monitoring frameworks for production of AI systems
Experience building reusable AI accelerators, enterprise AI frameworks, reference architectures, and shared platform services
Healthcare, financial services, insurance, or other regulated industry experience
Experience mentoring AI engineers, architects, and technical leaders within large enterprise environments
Proven deep expertise with Azure OpenAI, Azure AI Foundry, AWS Bedrock, Vertex AI, Anthropic, OpenAI, and open-source model ecosystems
Proven solid expertise in AI platform engineering, LLMOps, model lifecycle management, evaluation platforms, and enterprise AI governance
Proven expertise in AI security, AI risk management, compliance controls, and responsible AI deployment practices
Proven contributions to enterprise AI innovation programs, technical publications, patents, open-source projects, or industry thought leadership initiatives
Technical Stack
Foundation Models and LLM Platforms (OpenAI, Azure OpenAI, Claude, Gemini, Llama, Mistral, Phi, and similar models)
Generative AI Frameworks (LangChain, LangGraph, LlamaIndex, Semantic Kernel, AutoGen, CrewAI)
Model Development and Fine-Tuning Frameworks
Retrieval-Augmented Generation (RAG) and Vector Database Technologies
AI Evaluation, Observability, and Monitoring Platforms
Python-Based AI Application Development and API Integration
Cloud AI Platforms (Azure AI Foundry, Azure OpenAI, AWS Bedrock, Vertex AI)
Kubernetes, Docker, and Cloud-Native Deployments
LLMOps, DevOps, and AI Lifecycle Management
AI Gateway, API Management, and Multi-Model Platforms
Security, Governance, and Responsible AI Controls
At UnitedHealth Group, our mission is to help people live healthier lives and make the health system work better for everyone. We believe everyone-of every race, gender, sexuality, age, location and income-deserves the opportunity to live their healthiest life. Today, however, there are still far too many barriers to good health which are disproportionately experienced by people of color, historically marginalized groups and those with lower incomes. We are committed to mitigating our impact on the environment and enabling and delivering equitable care that addresses health disparities and improves health outcomes - an enterprise priority reflected in our mission.
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