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 GenAI Innovation & Technology Acceleration

Lead the exploration, evaluation, and application of emerging GenAI technologies, with a strong focus on Agentic AI, conversational agents, autonomous workflows, multi-agent systems, and LLM-enabled enterprise automation

Develop rapid prototypes, proof-of-concepts, technical accelerators, and reusable solution patterns that help accelerate AI adoption across business and technology teams

Assess new AI frameworks, orchestration patterns, model capabilities, evaluation techniques, and deployment approaches to determine enterprise applicability, scalability, and risk

Translate innovation concepts into practical engineering blueprints, reference implementations, and production-ready solutions

Agentic AI Solution Design & Implementation

Design and implement Agentic AI solutions using Python and modern AI orchestration frameworks such as LangChain, LangGraph, Semantic Kernel, AutoGen, CrewAI, ReACT, ReWOO, RAG, and agent-to-agent communication patterns

Build intelligent agents capable of reasoning, planning, tool usage, memory management, API interaction, multi-step workflow execution, and context-aware conversational experiences

Develop conversational AI systems that support multi-turn interactions, enterprise knowledge retrieval, user intent handling, task completion, personalization, and seamless integration with backend systems

Implement RAG-based architectures using vector databases, embeddings, document processing pipelines, semantic search, reranking, grounding, and context optimization techniques

Technical Engineering & Hands-On Delivery

Contribute directly to software design, application development, prompt engineering, agent orchestration, evaluation pipelines, and production implementation

Convert architectural guidance and innovation ideas into scalable, maintainable, secure, and well-tested engineering deliverables

Develop APIs, microservices, reusable libraries, and platform components that enable GenAI capabilities to be integrated into enterprise applications

Apply strong software engineering practices to ensure AI solutions are reliable, modular, extensible, observable, and production ready

Cloud-Native AI Development

Build and deploy secure, scalable, cloud-native AI solutions on Azure, using services such as Azure OpenAI, Azure AI Search, Azure Functions, Azure Kubernetes Service, Cosmos DB, Azure Container Apps, API Management, Key Vault, and related platform services

Design solutions using microservices, event-driven architectures, serverless patterns, containerized workloads, and scalable cloud infrastructure

Ensure AI solutions comply with enterprise standards for security, reliability, privacy, observability, resiliency, and operational excellence

LLMOps, Evaluation & AI Reliability

Develop evaluation frameworks for LLM and agentic systems, including response quality, factuality, hallucination risk, grounding accuracy, task completion rate, latency, cost, safety, and user experience

Implement guardrails, safety checks, content filtering, fallback strategies, prompt versioning,



model monitoring, and human-in-the-loop review mechanisms where appropriate

Create automated testing and benchmarking approaches for prompts, agents, tools, workflows, RAG pipelines, and conversational experiences

Continuously improve agent performance through experimentation, prompt refinement, retrieval optimization, model selection, tool design, and feedback loops

Performance Optimization

Analyze and improve the performance of AI systems, including response latency, throughput, token usage, retrieval accuracy, context window efficiency, cost optimization, and multi-turn conversation quality

Profile and optimize AI workflows across model calls, retrieval pipelines, API integrations, orchestration layers, and backend services

Identify engineering bottlenecks and implement improvements that enhance scalability, reliability, and user experience

DevOps, Deployment & Operational Readiness

Implement and manage containerized deployments using Docker, Kubernetes, AKS, and CI/CD pipelines

Build automated deployment workflows with quality gates, test automation, security scanning, environment promotion, and rollback strategies

Support production readiness through operational playbooks, runbooks, logging, monitoring, alerting, and incident triage

Apply DevOps, MLOps, and LLMOps principles to support continuous delivery and continuous improvement of AI solutions

Technical Leadership & Mentorship

Lead and mentor engineers working on GenAI, Agentic AI, and conversational AI solutions

Provide technical direction, conduct code reviews, guide design decisions, and promote engineering best practices

Help build team capability in modern AI engineering, prompt engineering, agent design, RAG, LLMOps, cloud-native development, and responsible AI practices

Contribute to technical communities of practice, internal knowledge sharing, reusable frameworks, and adoption playbooks

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 or Master's degree in Computer Science, Engineering, Data Science, Artificial Intelligence, Machine Learning, or a related technical field; equivalent practical experience will also be considered

8+ years of experience in software engineering, AI/ML engineering, platform engineering, or large-scale distributed systems

Hands-on experience with AI agent frameworks and LLM orchestration tools such as LangChain, LangGraph, Semantic Kernel, AutoGen, CrewAI, or similar frameworks

Experience designing and implementing RAG pipelines, including document ingestion, chunking, embeddings, vector search, semantic retrieval, reranking, grounding, and response generation

Experience with cloud-native development on Azure, including services such as Azure OpenAI, Azure AI Search,



Azure Functions, Cosmos DB, AKS, Key Vault, and API Management

Experience with containerization and orchestration using Docker, Kubernetes, and CI/CD pipelines

Experience with observability frameworks, telemetry, distributed tracing, performance monitoring, and production issue troubleshooting

Solid hands-on experience building AI/ML, GenAI, or LLM-powered applications in production or enterprise environments

Solid understanding of Agentic AI architectures, including planning, reasoning, tool usage, memory, function calling, agent orchestration, multi-agent collaboration, and workflow automation

Deep understanding of LLM-driven architectures, prompt engineering, prompt optimization, model evaluation, context management, and reliability guardrails

Solid knowledge of DevOps, MLOps, or LLMOps practices, including automated testing, deployment pipelines, monitoring, logging, and operational support

Solid testing discipline, including unit testing, integration testing, regression testing, prompt testing, and evaluation automation using tools such as pytest or equivalent frameworks

Proficiency in Python and solid understanding of software engineering fundamentals, including data structures, APIs, microservices, testing, observability, and scalable system design

Proven ability to benchmark and optimize AI applications for latency, throughput, accuracy, cost, scalability, and user experience

Proven ability to lead engineering teams, mentor developers, review code, guide technical decisions, and deliver high-quality solutions

Proven solid communication, collaboration, and stakeholder management skills, with the ability to translate business needs into technical solutions and explain complex AI concepts clearly Preferred Qualifications Experience building enterprise-grade conversational agents, virtual assistants, copilots, or AI-powered workflow automation solutions

Experience with Azure OpenAI, OpenAI APIs, Anthropic Claude, Google Gemini, open-source LLMs, or model hosting platforms

Experience with vector databases or search platforms such as Azure AI Search, Pinecone, Weaviate, FAISS, Milvus, Chroma, or Elasticsearch

Experience with evaluation and monitoring tools for LLM applications, such as LangSmith, PromptFlow, TruLens, Ragas, DeepEval, Arize Phoenix, or similar platforms

Experience designing reusable AI platforms, internal developer accelerators, SDKs, reference architectures, or enterprise AI enablement frameworks

Experience integrating AI agents with enterprise systems such as CRM, claims platforms, document management systems, knowledge bases, workflow engines, APIs, and microservices

Experience working in an innovation lab, accelerator, platform engineering team, applied AI group, or emerging technology function

Exposure to speech, voice, NLU, NLP, or multimodal AI capabilities Understanding of responsible AI principles, including fairness, transparency, privacy, security, safety, explainability, and human oversight 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.

📌 Senior AI/ML Engineer - Machine learning, Agentic AI, LangGraph (Chennai)
🏢 Optum India
📍 Chennai