Advance global health optimization by leading the design, implementation, and adoption of cutting-edge AI-ready data platforms. This role focuses on supporting modern AI/ML solutions, including Deep Learning, Generative AI, Copilots, RAG, and Agentic AI. You will leverage a strong data engineering background to build scalable and robust data infrastructure, ensuring enterprise AI products thrive from development through production.

Optum is dedicated to improving health outcomes through technology, fostering a culture of inclusion, and offering comprehensive career development. Join a team that is Caring, Connecting, and Growing together to make a significant impact.

Lead the end-to-end lifecycle of AI-ready enterprise data platforms, encompassing lakehouse, data lake, warehouse, streaming, and event-driven architectures. Define and implement scalable data engineering patterns for AI workloads, focusing on ingestion, curation, transformation, feature preparation, semantic access, and governed consumption.

Drive the successful delivery of AI/ML, Deep Learning, Generative AI, Copilot, RAG, and Agentic AI products. Implement MLOps, LLMOps, CI/CD, and automated testing practices to ensure robust model lifecycle management and production AI operations. Partner closely with data scientists, applied scientists, and business stakeholders to operationalize AI solutions at an enterprise scale.

Own the adoption of AI data platforms, defining and tracking key success metrics such as usage, reuse, onboarding time, and AI delivery acceleration. Enable data foundations for LLM applications, enterprise copilots, and semantic search through the implementation of embeddings pipelines and vector stores. Foster a culture of innovation and engineering excellence within AI/ML engineering teams.

A Bachelor's degree in Computer Science, Engineering, Data Science, Artificial Intelligence, Information Technology, or a related field is required, with a Master's degree being preferred. Possess over 15 years of experience in data engineering, AI/ML engineering, software engineering, or platform engineering, including more than 5 years of leadership experience managing engineering teams and large-scale enterprise technology delivery programs.

Demonstrate a strong hands-on background in designing and implementing enterprise data platforms for AI/ML, Deep Learning, GenAI, RAG, Copilot, and Agentic AI workloads. Proven experience in building AI-ready data capabilities and delivering AI/ML products and platforms from development through production deployment, monitoring, and scaling. Expertise in MLOps, LLMOps, AIDLC, CI/CD, model lifecycle management, observability, and production AI operations is essential.

Skills in Python, SQL, Spark, PySpark, and modern cloud data engineering frameworks are critical. Experience with major cloud platforms such as Azure, AWS, and/or GCP, along with distributed processing, APIs, microservices, orchestration, and production engineering practices, is expected. Strong stakeholder management, delivery leadership, communication, and problem-solving abilities are also key requirements.