Senior AI/ML Engineer at Universal Health Group

Gurgaon, haryana, India -

Full Time

Start Date

Immediate

Expiry Date

23 Sep, 26

Salary

0.0

Posted On

25 Jun, 26

Experience

10 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

GenAI, Agentic AI, Python, LangChain, RAG, Azure OpenAI, LLMOps, Kubernetes, Docker, Prompt Engineering, Vector Databases, Microservices, Software Engineering, API Design, MLOps, Technical Leadership

Industry

Hospitals and Health Care

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. 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 strong 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.

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Responsibilities

Lead the design and implementation of Agentic AI solutions and conversational agents using modern orchestration frameworks. Develop scalable, cloud-native AI architectures on Azure while establishing LLMOps evaluation frameworks and mentoring engineering teams.