Lead AI/ML Engineer

Optum India; Bengaluru; On-site; INR 4,000,000 - 8,000,000; Full time

Job summary

Optum India is seeking a Lead GenAI Specialist to provide technical leadership for enterprise Generative AI and Agentic AI initiatives. You will define architecture standards, lead multi-model deployments, and mentor teams to accelerate adoption of GenAI capabilities across the organization.

You will drive scalable AI platform design, governance, and cost-efficient implementations, collaborating with security, compliance, and business units to deliver impact at scale.

Qualifications

• Bachelor's degree in computer science, Artificial Intelligence, Machine Learning, Engineering, Data Science, or related field
• 12+ years of experience in AI, Machine Learning, Software Engineering, AI Platforms, or related disciplines
• 5+ years of hands-on experience delivering Generative AI and LLM-based solutions in production environments
• Proven experience leading complex enterprise GenAI and Agentic AI initiatives
• Hands-on experience with RAG architectures, semantic retrieval, vector search, and enterprise knowledge systems
• Experience building AI platforms, AI gateways, orchestration layers, and shared GenAI services
• Experience implementing AI evaluation, observability, governance, and Responsible AI practices
• Solid programming experience in Python and AI application development
• Experience with Azure, AWS, or GCP AI platforms
• Solid expertise in foundation models, transformer architectures, embeddings, tokenization, inference optimization, and context management
• Solid understanding of Agentic AI frameworks, tool integration, workflow automation, and multi-agent systems
• Proven excellent communication, stakeholder management, mentoring, and technical leadership skills

Responsibilities

• Define technical direction for enterprise Generative AI and Agentic AI initiatives
• Lead architecture decisions for complex AI programs and strategic business use cases
• Establish AI standards, reference architectures, governance models, and engineering best practices
• Evaluate emerging foundation models, frameworks, and AI platforms to guide enterprise adoption
• Mentor GenAI Specialists, engineers, and AI practitioners while leading small delivery teams
• Design scalable GenAI solutions aligned with business, security, compliance, and operational requirements
• Architect multi-model solutions leveraging proprietary and open-source foundation models
• Define reusable patterns for AI assistants, copilots, intelligent search, automation, and decision-support systems
• Create reference implementations, accelerators, and reusable frameworks to accelerate enterprise adoption
• Lead foundation model evaluation, benchmarking, selection, fine-tuning, and deployment strategies
• Define standards for model adaptation techniques including SFT, LoRA, QLoRA, PEFT, and domain tuning
• Optimize model quality, latency, throughput, scalability, and cost efficiency
• Govern model lifecycle processes including evaluation, validation, release management, and production readiness
• Architect enterprise RAG solutions supporting large-scale knowledge retrieval and GenAI applications
• Design retrieval frameworks including ingestion, chunking, embeddings, indexing, semantic search, reranking, and grounding
• Establish enterprise patterns for vector search, hybrid retrieval, knowledge graphs, and AI-ready content platforms
• Define evaluation frameworks to improve retrieval quality, groundedness, and response accuracy
• Design and govern enterprise Agentic AI architectures
• Build autonomous and human-in-the-loop workflows using tool integration, planning, reasoning, and orchestration frameworks
• Lead development of single-agent and multi-agent systems for complex business processes
• Establish standards for agent memory, context management, state handling, and agent observability
• Define architecture for enterprise AI platforms, gateways, model orchestration, and shared AI services
• Drive LLMOps practices including deployment automation, monitoring, evaluation, governance, and lifecycle management
• Establish secure integration patterns across applications, APIs, data platforms, and business workflows
• Partner with platform and engineering teams to operationalize scalable AI capabilities
• Define enterprise standards for AI evaluation, observability, monitoring, and quality measurement
• Establish controls for Responsible AI, model governance, safety, security, compliance, and risk management
• Implement guardrails for hallucination mitigation, content safety, prompt security, and data protection
• Drive AI cost optimization through model routing, caching, prompt engineering, and workload optimization
• Lead enterprise AI innovation and capability-building initiatives
• Influence AI roadmaps, platform investments, and architectural direction
• Drive adoption of reusable AI frameworks, accelerators, and shared services
• Promote knowledge sharing and technical excellence across teams

Skills

Technical leadership

GenAI architectures

Mentoring

Python programming

Stakeholder management

AI application development

Education

Bachelor's degree in Computer Science, AI, ML, Engineering, Data Science, or related field

Master's degree

Tools

Python

Azure

AWS

GCP

LangChain

LangGraph

Semantic Kernel

LlamaIndex

AutoGen

CrewAI

Pinecone

Weaviate

FAISS

Chroma

pgvector

Job description

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.

Caring. Connecting. Growing together.

We are seeking a Lead GenAI Specialist to provide technical leadership for enterprise Generative AI and Agentic AI initiatives. This role is responsible for driving architecture decisions, defining reusable AI platforms and frameworks, guiding complex solution delivery, and accelerating adoption of GenAI capabilities across the organization.

Success will be measured through business impact, platform adoption, scalability of AI solutions, reusable enterprise capabilities, and advancement of organizational AI maturity.

Primary Responsibilities

• AI Strategy and Technical Leadership
  • Define technical direction for enterprise Generative AI and Agentic AI initiatives
  • Lead architecture decisions for complex AI programs and strategic business use cases
  • Establish AI standards, reference architectures, governance models, and engineering best practices
  • Evaluate emerging foundation models, frameworks, and AI platforms to guide enterprise adoption
  • Mentor GenAI Specialists, engineers, and AI practitioners while leading small delivery teams
• Enterprise GenAI Solution Architecture
  • Design scalable GenAI solutions aligned with business, security, compliance, and operational requirements
  • Architect multi-model solutions leveraging proprietary and open-source foundation models
  • Define reusable patterns for AI assistants, copilots, intelligent search, automation, and decision-support systems
  • Create reference implementations, accelerators, and reusable frameworks to accelerate enterprise adoption
• Foundation Models and Model Engineering
  • Lead foundation model evaluation, benchmarking, selection, fine-tuning, and deployment strategies
  • Define standards for model adaptation techniques including SFT, LoRA, QLoRA, PEFT, and domain tuning
  • Optimize model quality, latency, throughput, scalability, and cost efficiency
  • Govern model lifecycle processes including evaluation, validation, release management, and production readiness
• Knowledge Systems
  • Architect enterprise RAG solutions supporting large-scale knowledge retrieval and GenAI applications
  • Design retrieval frameworks including ingestion, chunking, embeddings, indexing, semantic search, reranking, and grounding
  • Establish enterprise patterns for vector search, hybrid retrieval, knowledge graphs, and AI-ready content platforms
  • Define evaluation frameworks to improve retrieval quality, groundedness, and response accuracy
• Agentic AI and Intelligent Automation
  • Design and govern enterprise Agentic AI architectures
  • Build autonomous and human-in-the-loop workflows using tool integration, planning, reasoning, and orchestration frameworks
  • Lead development of single-agent and multi-agent systems for complex business processes
  • Establish standards for agent memory, context management, state handling, and agent observability
• AI Platforms, LLMOps and Integration
  • Define architecture for enterprise AI platforms, gateways, model orchestration, and shared AI services
  • Drive LLMOps practices including deployment automation, monitoring, evaluation, governance, and lifecycle management
  • Establish secure integration patterns across applications, APIs, data platforms, and business workflows
  • Partner with platform and engineering teams to operationalize scalable AI capabilities
• AI Quality, Governance and Responsible AI
  • Define enterprise standards for AI evaluation, observability, monitoring, and quality measurement
  • Establish controls for Responsible AI, model governance, safety, security, compliance, and risk management
  • Implement guardrails for hallucination mitigation, content safety, prompt security, and data protection
  • Drive AI cost optimization through model routing, caching, prompt engineering, and workload optimization
• Organizational Impact
  • Lead enterprise AI innovation and capability-building initiatives
  • Influence AI roadmaps, platform investments, and architectural direction
  • Drive adoption of reusable AI frameworks, accelerators, and shared services
  • Promote knowledge sharing and technical excellence across teams
• 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, Engineering, Data Science, or related field
• 12+ years of experience in AI, Machine Learning, Software Engineering, AI Platforms, or related disciplines
• 5+ years of hands-on experience delivering Generative AI and LLM-based solutions in production environments
• Proven experience leading complex enterprise GenAI and Agentic AI initiatives
• Hands-on experience with RAG architectures, semantic retrieval, vector search, and enterprise knowledge systems
• Experience building AI platforms, AI gateways, orchestration layers, and shared GenAI services
• Experience implementing AI evaluation, observability, governance, and Responsible AI practices
• Solid programming experience in Python and AI application development
• Experience with Azure, AWS, or GCP AI platforms
• Solid expertise in foundation models, transformer architectures, embeddings, tokenization, inference optimization, and context management
• Solid understanding of Agentic AI frameworks, tool integration, workflow automation, and multi-agent systems
• Proven excellent communication, stakeholder management, mentoring, and technical leadership skills

Preferred Qualifications

• Master's degree
• Advanced degree in Artificial Intelligence, Machine Learning, Computer Science, or related field
• Experience leading small teams of GenAI engineers, architects, or specialists
• Experience with LangChain, LangGraph, Semantic Kernel, LlamaIndex, AutoGen, CrewAI, or similar frameworks
• Experience building enterprise AI platforms, gateways, model catalogs, and reusable AI services
• Experience with vector databases such as Pinecone, Weaviate, FAISS, Chroma, pgvector, Azure AI Search, and Neo4j
• Experience implementing enterprise LLMOps, AI governance, AI security, and compliance frameworks
• Experience building AI accelerators, shared frameworks, and enterprise AI reference architectures
• Healthcare, insurance, financial services, or other regulated industry experience
• Expertise with Azure OpenAI, Azure AI Foundry, Bedrock, Vertex AI, Anthropic, OpenAI, and open-source model ecosystems
• Expertise in advanced RAG architectures including hybrid search, graph retrieval, reranking, and knowledge graphs
• Proven contributions to patents, publications, enterprise innovation programs, or AI thought leadership initiatives

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.