Teams at Lockheed Martin

Build and deploy production AI systems centered on ontologies, knowledge graphs, and agent workflows. Translate enterprise data and user needs into semantic models, graph systems, and reliable software. Develop evaluation, testing, observability, security, and deployment controls across AI, data, application, and infrastructure layers. Work directly with domain experts, lead technical delivery, mentor engineers, and convert customer engagements into reusable platform capabilities.

Lead architecture and delivery of production AI systems involving agents, ontologies, knowledge graphs, semantic models, and enterprise data integrations. Translate ambiguous user needs into secure, reliable capabilities; establish governance, evaluation, traceability, and human-review practices; guide deployments across cloud platforms, APIs, databases, and applications; resolve complex system issues; mentor engineers; and convert customer-specific solutions into reusable platform capabilities.

Leads the design, development, governance, and productization of enterprise data models, pipelines, data products, and ontologies. Builds machine-learning-ready datasets, implements security and lineage controls, optimizes Databricks and Delta Lake environments, establishes data-product standards, collaborates with architects and data scientists, and mentors engineering talent. The role also defines product roadmaps, service levels, usage metrics, and continuous-improvement processes for enterprise data assets supporting AI agents, analytics, and applications.