Senior AI/ML Engineer

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
  • Implement monitoring and governance strategies for AI applications, models, prompts, agents, and workflows
• 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
  • Integrate AI capabilities with enterprise applications, APIs, business processes, and operational systems
  • Optimize AI systems for response quality, accuracy, latency, throughput, and efficiency
• Backend Systems & Distributed Architecture
  • Design and develop high-performance backend applications using modern software engineering principles
  • 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
  • Monitor model performance, inference behavior, agent effectiveness, application health, and operational KPIs
  • 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 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, Engineering, Artificial Intelligence, Data Science, or related field; Master's degree preferred
• 12+ years of software engineering, AI engineering, platform engineering, or backend systems development experience
• Experience implementing MLOps, LLMOps, and AI operational practices in enterprise environments
• Experience building and deploying AI/ML solutions into production environments
• Solid backend engineering experience designing and developing scalable enterprise applications and distributed systems
• Hands-on experience with:
  • o   Large Language Models (LLMs)
  • o   Retrieval-Augmented Generation (RAG)
  • o   Embeddings
  • o   Vector databases
  • o   Agentic AI systems
• Experience building APIs, microservices, distributed systems, and event-driven architectures
• Experience developing cloud-native applications on Azure, AWS, and/or Google Cloud Platform
• Experience with monitoring, observability, reliability engineering, and production operations
• Solid understanding of containerization, Kubernetes, deployment automation, and infrastructure engineering
• Solid expertise in Machine Learning, AI system development, model deployment, and inference architectures
• Solid programming expertise in Python and modern backend development frameworks
• Proven solid communication, analytical, leadership, and stakeholder management skills

Preferred Qualifications:

• Experience building enterprise AI platforms supporting AI/ML, Generative AI, LLM, and Agentic AI workloads
• Experience with AI orchestration frameworks such as LangChain, LangGraph, Semantic Kernel, AutoGen, CrewAI, or LlamaIndex
• Experience deploying and managing open-source and commercial foundation models
• Experience designing and implementing RAG architectures, vector retrieval systems, semantic search platforms, and enterprise knowledge solutions
• Experience with Kafka, Spark, Databricks, Redis, Elasticsearch, or equivalent platform technologies
• Experience implementing AI observability, prompt monitoring, agent monitoring, evaluation frameworks, and operational governance
• Experience developing reusable platform capabilities, engineering frameworks, shared services, and architecture accelerators
• Experience within healthcare, financial services, insurance, or other highly regulated industries
• Experience leading engineering teams and driving large-scale technology transformation initiatives
• Solid understanding of Responsible AI, model governance, AI security, and enterprise compliance requirements
• Solid expertise with MLOps and LLMOps platforms including MLflow, Kubeflow, Azure ML, SageMaker, Vertex AI, or equivalent technologies
• Expertise in distributed computing, event-driven architectures, messaging systems, and real-time data processing
• Contributions to enterprise AI platforms, technical publications, patents, open-source projects, or innovation programs

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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