Dir, AI/ML Engineering - Remote

UnitedHealth Group•Eden Prairie, MN

2d•$176,700 - $302,900•Remote

About The Position

The Director, AI/ML Engineering – AI-Ready Data and Context Platforms will lead the strategy, architecture, and delivery of an enterprise context layer that supports generative AI and agentic workflows. The role makes enterprise data AI-ready through semantic models, ontologies, knowledge graphs, metadata capabilities, and reusable contextual data products. The director will partner with AI, data, product, architecture, governance, and business teams so AI agents can use trusted, relevant, secure, and understandable information. The role leads multidisciplinary engineering teams and establishes standards for context engineering, semantic interoperability, data quality, governance, and AI grounding. This is a remote position available anywhere in the United States.

Requirements

Bachelor’s degree in Computer Science, Artificial Intelligence, Data Science, Engineering, or a related field.
10+ years of experience in AI/ML engineering, data engineering, data architecture, knowledge engineering, or semantic technologies.
5+ years of experience leading technical teams, influencing senior stakeholders, and translating business needs into platform capabilities.
5+ years of experience leading enterprise-scale data, AI, or knowledge-platform initiatives.
3+ years of experience designing semantic models, ontologies, taxonomies, or knowledge graphs.
3+ years of experience integrating structured and unstructured data into scalable AI-ready platforms.
Demonstrated understanding of data products, metadata, lineage, data quality, governance, and access controls.
Demonstrated understanding of generative AI, large language models, retrieval-augmented generation, vector search, and agentic AI workflows.
Remote employees must adhere to UnitedHealth Group’s Telecommuter Policy.
Candidates must pass a drug test before beginning employment.

Nice To Haves

Master’s degree in Computer Science, Artificial Intelligence, Data Science, Information Science, or a related field.
Experience implementing enterprise knowledge graphs, semantic layers, or ontology-management platforms.
Experience with hybrid retrieval approaches combining graph, vector, keyword, metadata, and structured-data search.
Experience in a regulated industry or an environment with complex security, privacy, and compliance requirements.
Familiarity with semantic technologies and standards such as RDF, OWL, SHACL, SKOS, SPARQL, or JSON-LD.
Knowledge of GraphRAG, agent memory, context management, model context protocols, and AI tool-integration patterns.

Responsibilities

Lead and direct AI/ML engineering teams in designing, building, and delivering enterprise-scale machine learning systems and intelligent platforms.
Partner with senior technology, data, analytics, product, and business leaders to embed semantic and contextual foundations into enterprise AI strategy.
Define and own the enterprise strategy and roadmap for AI-ready data, semantic architecture, knowledge graphs, and contextual data platforms.
Establish the architectural vision for an enterprise context layer supporting generative AI applications, intelligent search, decision-support systems, and agentic workflows.
Identify semantic, contextual, and data-quality capabilities that enable AI agents to understand business entities, relationships, processes, policies, terminology, and constraints.
Translate enterprise AI priorities into a sequenced portfolio of foundational data, metadata, ontology, and knowledge-graph capabilities.
Use enterprise-approved AI tools to streamline workflows, automate tasks, and drive continuous improvement.
Evaluate emerging trends to inform solution design, platform scalability, and strategic technical innovation.
Mentor and develop technical leaders and engineering talent to support technical growth and team capability.