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 :

- AI Systems Architecture & Engineering

- Design, develop, and operate enterprise AI systems supporting Machine Learning, Generative AI, Agentic AI, and intelligent automation workloads

- Lead architecture and implementation of scalable backend systems enabling AI model serving, inference, orchestration, and enterprise integration

- Design reusable AI services, APIs, microservices, and platform components that accelerate AI solution delivery

- Develop AI systems that support both real-time and batch inference workloads with high availability and low latency

- Translate business requirements into scalable, secure, and production-ready AI solutions

- AI Platform Engineering

- Build and enhance enterprise AI platforms supporting:

- Model development

- Model deployment

- Model serving

- Inference orchestration

- Evaluation

- Governance

- Lifecycle management

- Design AI platform capabilities that enable self-service AI deployment and operationalization

- Develop reusable platform services, SDKs, accelerators, deployment templates, and engineering frameworks

- Support centralized AI governance, model access, usage management, and operational controls

- MLOps, LLMOps & Agentic Ops





- Establish and manage end-to-end MLOps, LLMOps, and Agentic Ops practices

- Build automated CI/CD pipelines supporting:

- Model deployment

- Agent deployment

- Application deployment

- Infrastructure deployment

- Implement model lifecycle management, experiment tracking, versioning, validation, rollout strategies, and release management

- Operationalize LLMs, RAG systems, AI agents, and multi-agent workflows within enterprise environments

- Generative AI & Agentic AI Engineering

- Design and implement enterprise Generative AI solutions using:

- Large Language Models (LLMs)

- Retrieval-Augmented Generation (RAG)

- Prompt orchestration

- Semantic retrieval

- Vector search

- Build Agentic AI systems capable of:

- Multi-step reasoning

- Workflow execution

- Tool usage

- API integrations

- Multi-agent orchestration

- Optimize AI systems for response quality, accuracy, latency, throughput, and efficiency

- Backend Systems & Distributed Architecture

- Build scalable distributed systems that support AI workloads across cloud and hybrid environments

- Develop event-driven, service-oriented, and microservices-based architectures

- Implement caching, asynchronous processing, messaging, queuing, and workload distribution strategies

- Optimize backend services for scalability, resilience, fault tolerance, and operational efficiency

- Observability, Reliability & Governance





- Establish observability frameworks for AI platforms and services including:

- Logging

- Monitoring

- Distributed tracing

- Alerting

- Operational telemetry

- Implement Responsible AI controls, model governance, security safeguards, and compliance requirements

- Support incident management, root cause analysis, production troubleshooting, and continuous reliability improvements

- Cross-Functional Leadership

- Collaborate with Applied Scientists, Data Scientists, Data Engineers, Platform Engineers, Security Teams, and Product Organizations

- Establish engineering standards, best practices, architecture patterns, and operational guidelines

- Mentor engineers and drive technical excellence across AI engineering teams

- Stay current with emerging technologies across AI, GenAI, Agentic AI, cloud platforms, and backend engineering ecosystems.

- 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 workplace, 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 - Scientist Responsibilities : (manager discretion if AI responsibilities are already present in JD):

- 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

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📌 Senior Ai/ml Engineer (Bengaluru)
🏢 UnitedHealth Group
📍 Bengaluru