Optum is a global healthcare organization dedicated to improving lives through technology. Join a culture of inclusion and collaboration where you'll work with talented peers and find extensive benefits and career development opportunities. Advance health optimization globally and make a significant impact on the communities we serve.

We are seeking a hands-on AI/ML Engineer to design, build, deploy, and support innovative AI solutions. This role emphasizes transforming AI/ML, Generative AI, and Agentic AI into scalable, secure, and production-ready applications. The ideal candidate will possess strong machine learning fundamentals, robust software engineering skills, and experience with modern AI platforms. You will collaborate closely with Data Scientists, Applied Scientists, Data Engineers, and Platform Engineers to operationalize AI capabilities and drive enterprise AI adoption.

UnitedHealth Group is committed to helping individuals achieve healthier lives and enhancing the healthcare system for everyone. We champion equitable care and strive to overcome barriers to good health, particularly for marginalized communities. Our mission reflects a dedication to environmental sustainability and addressing health disparities.

Develop and deploy AI/ML solutions adhering to AI Development Lifecycle (AIDLC) practices, translating business needs into scalable AI applications. Build, train, evaluate, and deploy machine learning models for enterprise use cases, supporting production-grade AI systems that include data ingestion, feature engineering, model training, deployment, and monitoring.

Engineer AI services and applications using APIs, microservices, and cloud-native architectures. Implement automated deployment and operational workflows for reliable AI delivery, managing model lifecycles including deployment, monitoring, retraining, and version control. Contribute to reusable engineering frameworks and automation assets.

Build and integrate Generative AI capabilities, supporting solutions with Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), embeddings, and prompt engineering. Assist in developing Agentic AI workflows involving multi-step tasks, tool integrations, and human-in-the-loop processes. Optimize AI response quality and operational performance.

Implement MLOps and LLMOps practices, including experiment tracking, deployment automation, CI/CD integration, and monitoring/observability. Monitor AI systems for performance, accuracy, drift, and reliability. Participate in troubleshooting and production support, ensuring robust logging, monitoring, and alerting.

Collaborate with data engineering teams for data readiness and quality. Partner with AI, platform, and product teams to deploy and operate AI solutions at scale. Integrate AI capabilities into enterprise systems and business workflows, contributing to scalable architecture patterns.

Adhere to Responsible AI, governance, security, and compliance requirements. Support model validation, explainability, and auditability. Comply with all company policies, procedures, and directives.

Possess a Bachelor's degree in Computer Science, Engineering, Data Science, Artificial Intelligence, Mathematics, or a related field. Bring over 5 years of experience in AI/ML Engineering, Data Science, Software Engineering, or related disciplines.

Demonstrate experience building and deploying machine learning solutions in enterprise or cloud environments. Proven ability to develop AI pipelines covering data preparation, model training, evaluation, and deployment. Expertise in developing APIs, microservices, and cloud-based AI services.

Proficiency with cloud platforms such as Azure, AWS, and/or GCP. Experience with Generative AI technologies including Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), embeddings, and prompt engineering. Solid understanding of machine learning, statistical modeling, feature engineering, model evaluation, and AI/ML lifecycle management.

Familiarity with software engineering principles, testing practices, and production operations. Understanding of MLOps practices, including monitoring, deployment automation, and model lifecycle management. Strong programming skills in Python and SQL. Excellent analytical, problem-solving, communication, and collaboration skills.