Senior AI/ML Engineer

Requisition Number: 2369774
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 Senior AI/ML Engineer to design, build, deploy, and scale enterprise AI solutions from experimentation through production. This role is focused on operationalizing AI through a robust AI Development Lifecycle (AIDLC), ensuring that AI/ML, Generative AI, and Agentic AI solutions are engineered for reliability, scalability, performance, and business impact.
You will work closely with data scientists, applied scientists, platform engineers, and business stakeholders to transform validated AI concepts into secure, production-ready systems. The ideal candidate combines strong software engineering fundamentals with expertise in AI/ML platforms, MLOps/LLMOps practices, cloud-native architectures, and enterprise-scale AI deployment.
Primary Responsibilities:

• Design, develop, and deploy end-to-end AI/ML solutions following AI Development Lifecycle (AIDLC) best practices
• Build production-grade AI systems including:
  • Data ingestion and processing pipelines
  • Feature engineering pipelines
  • Model training and evaluation workflows
  • Model deployment and monitoring solutions
• Develop scalable AI services and applications using:
  • APIs and microservices
  • Batch and real-time processing architectures
  • Cloud-native deployment patterns
• Build and maintain reliable, secure, and high-performance AI platforms capable of supporting enterprise-scale workloads
• Implement MLOps and LLMOps best practices, including:
  • CI/CD automation
  • Model lifecycle management
  • Experiment tracking
  • Automated testing and validation
  • Continuous monitoring and observability
• Develop reusable engineering frameworks, templates, and automation components that accelerate AI solution delivery
• Build and operationalize Generative AI solutions leveraging:
  • Large Language Models (LLMs)
  • Retrieval-Augmented Generation (RAG)
  • Prompt orchestration pipelines
  • Semantic search and retrieval systems
• Integrate LLM and GenAI capabilities into enterprise applications and business workflows
• Support development and deployment of Agentic AI workflows, including:
  • Multi-step task orchestration
  • Tool and API integrations
  • Human-in-the-loop workflows
• Ensure data readiness, quality, and pipeline reliability across AI use cases
• Partner with data engineering teams to integrate AI workloads with enterprise data platforms and governance standards
• Optimize AI systems for scalability, performance, reliability, and cost efficiency through:
  • Token optimization
  • Prompt optimization
  • Caching strategies
  • Infrastructure utilization improvements
• Implement software engineering best practices including:
  • Code quality standards
  • Automated testing
  • Code reviews
  • Documentation
  • Observability and monitoring
• Design and implement scalable architecture patterns that support enterprise AI adoption
• Collaborate with cross-functional teams across AI, engineering, product, platform, and business functions to deliver production-ready AI capabilities
• Measure and continuously improve solution effectiveness through operational metrics, performance monitoring, and business outcomes
• Comply with organizational policies, security standards, governance requirements, and Responsible AI principles
• 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

Technical Skills

• Programming: Python, SQL
• AI/ML: ML models, deep learning (PyTorch / TensorFlow)
• Generative AI: LLMs, RAG, prompt engineering, vector databases
• Agentic AI: Multi-agent frameworks, orchestration, tool integration
• Data Handling: Feature engineering, data pipelines, experimentation
• Cloud Platforms: Azure / AWS / GCP
• Optimization: Token usage, model selection, cost-performance tuning
• Tools: Experimentation frameworks, AI toolchains, prototyping platforms

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 throughout the model development lifecycle.
Required Qualifications:

• Bachelor's degree in computer science, Engineering, Data Science, Artificial Intelligence, Mathematics, or related field; master's degree preferred
• 12+ years of experience designing, building, and deploying enterprise AI/ML solutions
• Hands-on experience building and operationalizing machine learning models and AI systems
• Solid programming experience in Python and SQL
• Experience delivering production-grade AI applications and services at enterprise scale
• Experience implementing MLOps and LLMOps practices, including CI/CD, deployment automation, model monitoring, and lifecycle management
• Experience with Generative AI technologies including:
  • Large Language Models (LLMs)
  • Retrieval-Augmented Generation (RAG)
  • Prompt Engineering
  • Embeddings and vector search
• Experience integrating AI capabilities into enterprise applications and workflows
• Experience developing APIs, microservices, and distributed applications
• Experience with cloud platforms including Azure, AWS, and/or Google Cloud Platform
• Experience optimizing AI solutions for performance, scalability, reliability, and cost efficiency
• Solid understanding of software development lifecycle, testing frameworks, monitoring, and production operations
• Solid expertise in AI/ML engineering, software engineering, and cloud-native solution development
• Knowledge of feature engineering, data pipelines, model evaluation, and experimentation methodologies
• Proven solid analytical, communication, stakeholder management, and problem-solving skills
• Proven ability to collaborate effectively across engineering, AI, platform, and business teams

Preferred Qualifications:

• Experience building and scaling enterprise AI platforms and production AI applications
• Experience with MLOps and LLMOps platforms such as MLflow, Kubeflow, SageMaker, Azure ML, Vertex AI, or similar technologies
• Experience developing cloud-native applications using containerization, Kubernetes, serverless computing, and modern deployment architectures
• Experience implementing Retrieval-Augmented Generation (RAG), semantic search, vector databases, and enterprise knowledge retrieval solutions
• Experience with distributed systems, streaming architectures, and large-scale data processing platforms such as Spark, Kafka, and Databricks
• Experience implementing observability, monitoring, logging, and operational support practices for AI systems
• Experience developing reusable engineering frameworks, accelerators, shared services, and reference architectures
• Experience working in healthcare, financial services, insurance, or other regulated enterprise environments
• Experience mentoring engineers and driving engineering standards, best practices, and technical excellence
• Familiarity with AI orchestration frameworks such as LangChain, LlamaIndex, Semantic Kernel, LangGraph, CrewAI, AutoGen, or equivalent technologies
• Understanding of Responsible AI, model governance, security, and compliance requirements
• Contributions to enterprise AI platforms, technical publications, patents, open-source initiatives, 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.