AI/ML Engineer

Requisition Number: 2369783
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.
We're looking for a hands-on AI/ML Engineer to design, build, deploy, and support AI solutions using an AI Development Lifecycle (AIDLC) approach. This role focuses on transforming AI/ML, Generative AI, and Agentic AI solutions into scalable, secure, and production-ready applications.
The ideal candidate combines strong machine learning fundamentals, software engineering skills, and experience with modern AI platforms. You will work closely with Data Scientists, Applied Scientists, Data Engineers, and Platform Engineers to operationalize AI capabilities and support enterprise AI adoption.
Primary Responsibilities:

• AI/ML Solution Development
  • Develop and deploy AI/ML solutions following AI Development Lifecycle (AIDLC) practices
  • Translate business requirements into scalable AI-powered solutions
  • Build, train, evaluate, and deploy machine learning models for enterprise use cases
  • Support the development of production-grade AI systems including:
    • Data ingestion and processing pipelines
    • Feature engineering workflows
    • Model training and evaluation pipelines
    • Model deployment and monitoring solutions
  • Contribute to solution experimentation, validation, and continuous improvement
• AI Engineering & Production Deployment
  • Develop AI services and applications using:
    • APIs and microservices
    • Batch and real-time processing patterns
    • Cloud-native deployment architectures
  • Implement deployment automation and operational workflows that support reliable AI delivery
  • Support model lifecycle management including deployment, monitoring, retraining, and version control
  • Contribute to reusable engineering frameworks, templates, and automation assets
• Generative AI & Agentic AI
  • Build and integrate Generative AI capabilities into enterprise applications and workflows
  • Support solutions leveraging:
    • Large Language Models (LLMs)
    • Retrieval-Augmented Generation (RAG)
    • Embeddings and vector search
    • Prompt engineering
    • Semantic retrieval
  • Assist with development of Agentic AI workflows involving:
    • Multi-step task execution
    • Tool and API integrations
    • Human-in-the-loop processes
  • Support evaluation and optimization of AI response quality and operational performance
• MLOps, Monitoring & Reliability
  • Implement MLOps and LLMOps practices including:
    • Experiment tracking
    • Deployment automation
    • CI/CD integration
    • Monitoring and observability
  • Monitor AI systems for:
    • Performance
    • Accuracy
    • Drift
    • Reliability
    • Operational health
  • Participate in troubleshooting, root cause analysis, and production support activities
  • Support implementation of logging, monitoring, tracing, and alerting capabilities
• Data & Platform Collaboration
  • Partner with data engineering teams to ensure data readiness, quality, and reliability
  • Collaborate with AI, platform, and product teams to deploy and operate AI solutions at scale
  • Integrate AI capabilities into enterprise systems, applications, and business workflows
  • Contribute to scalable architecture patterns supporting AI adoption across the organization
• Security, Governance & Responsible AI
  • Follow Responsible AI, governance, security, and compliance requirements
  • Support model validation, explainability, and auditability activities
  • Adhere to enterprise engineering standards, development practices, and operational controls
• 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

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 throughout the model development lifecycle.
Please confirm once your respective roles have been updated. Reach out if you face any challenges.
Required Qualifications:

• Bachelor's degree in computer science, Engineering, Data Science, Artificial Intelligence, Mathematics, or related field
• 5+ years of experience in AI/ML Engineering, Data Science, Software Engineering, or related disciplines
• Experience building and deploying machine learning solutions in enterprise or cloud environments
• Experience developing AI pipelines including data preparation, model training, evaluation, and deployment
• Experience with Generative AI technologies including:
  • Large Language Models (LLMs)
  • Retrieval-Augmented Generation (RAG)
  • Embeddings
  • Prompt Engineering
• Experience developing APIs, microservices, and cloud-based AI services
• Experience with cloud platforms such as Azure, AWS, and/or GCP
• Solid programming skills in Python and SQL
• Solid understanding of:
  • Machine Learning
  • Statistical Modeling
  • Feature Engineering
  • Model Evaluation
  • AI/ML Lifecycle Management
• Understanding software engineering principles, testing practices, and production operations
• Familiarity with MLOps practices including monitoring, deployment automation, and model lifecycle management
• Proven solid analytical, problem-solving, communication, and collaboration skills

Preferred Qualifications:

• Experience building and deploying production of AI applications and services
• Experience with MLOps and AI platforms such as MLflow, Kubeflow, SageMaker, Azure ML, Vertex AI, or similar technologies
• Experience with Generative AI and Agentic AI application development
• Experience implementing RAG solutions, semantic search, vector databases, and knowledge retrieval architectures
• Experience with distributed data processing and large-scale data platforms including Spark, Databricks, Kafka, and cloud-native data services
• Experience implementing monitoring, observability, and operational support practices for AI systems
• Experience contributing to reusable AI frameworks, engineering accelerators, and platform capabilities
• Experience working within healthcare, financial services, insurance, or other regulated industries
• Experience mentoring junior engineers and contributing to engineering best practices
• Familiarity with AI orchestration frameworks such as LangChain, LangGraph, LlamaIndex, Semantic Kernel, CrewAI, AutoGen, or equivalent platforms
• Understanding of Responsible AI, model governance, security, and compliance requirements

Technical Skills

• Programming: Python, SQL
• Backend & APIs: FastAPI, Flask, REST APIs, microservices
• AI/ML: Supervised/unsupervised models, basic deep learning (PyTorch / TensorFlow)
• Generative AI: LLM APIs, prompt engineering, RAG pipelines, embeddings
• Agentic AI: Basic orchestration, tool integration (guided usage)
• Data Handling: Feature engineering, ETL pipelines, data preprocessing
• Cloud Platforms: AWS / Azure / GCP (model deployment, storage, APIs)
• MLOps / LLMOps: CI/CD pipelines, model versioning, deployment workflows
• Optimization: Token usage awareness, inference optimization, caching
• Monitoring & Observability: Logging, metrics, model monitoring basics
• Containers & Deployment: Docker, basic Kubernetes exposure

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.