Join Optum, a global health leader dedicated to improving lives through technology and innovative care. We are seeking a Director of AI/ML Engineering to spearhead our enterprise Data & AI Platform Strategy. This role is crucial for building scalable, secure, and efficient platforms supporting analytics, machine learning, generative AI, and agentic AI. You will shape the future of health optimization on a global scale.

Our culture thrives on inclusion, collaboration, and career development. We offer comprehensive benefits and opportunities to make a significant impact. Come contribute to our mission of Caring, Connecting, and Growing together.

This leadership position involves defining and executing the enterprise Data & AI Platform Strategy, ensuring robust foundations for advanced analytics and AI. You will lead data engineering teams in developing ingestion, transformation, storage, and real-time data solutions. Establishing engineering standards, driving cloud-native modernization, and partnering with Data Science teams to operationalize AI models are key. Responsibilities include enabling capabilities like feature stores, vector databases, and RAG. You will also drive enterprise data governance, promote a Data-as-a-Product model, and evaluate emerging technologies like GenAI and Agentic AI.

Key duties include prioritizing platform initiatives, presenting technology roadmaps to leadership, managing budgets and vendors, and optimizing cloud spend. Establishing performance KPIs, building high-performing teams, and fostering a culture of innovation and continuous learning are paramount. Compliance with all company policies and directives is required.

We are looking for extensive leadership experience in Data Engineering, Data Platforms, AI/ML Platforms, or Enterprise Data Architecture. A proven track record in implementing Data-as-a-Product frameworks and enterprise data strategies is essential. Experience managing large-scale engineering organizations, budgets, and cloud cost optimization is required.

Solid expertise in cloud data ecosystems and modern data architectures is necessary, along with deep knowledge of data engineering concepts and AI/ML operationalization (MLOps, GenAI, Agentic AI, Vector Databases, RAG, Embeddings, Model Deployment, Responsible AI). A strong understanding of enterprise data governance, data quality, lineage, metadata, MDM, privacy, and compliance is crucial. Familiarity with emerging technologies like Data Fabric, Knowledge Graphs, Semantic Layers, and Intelligent Automation is beneficial.

Excellent stakeholder management and executive communication skills are vital. Demonstrated success in building and leading high-performing teams across data engineering, analytics, governance, and AI domains, as well as driving platform modernization and digital transformation, are required.