Jobs 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.

Build and deploy production AI systems focused on ontologies, semantic models, and knowledge graphs. Translate enterprise data and user needs into AI agent workflows, software services, APIs, and graph systems. Develop evaluation, testing, observability, security, and deployment controls for AI and data products. Work directly with domain experts, lead technical delivery, resolve production issues, present architectural decisions, review code, mentor engineers, and convert customer engagements into reusable platform capabilities.

Build and operate an enterprise AI/ML platform, developing automation, infrastructure-as-code, CI/CD and GitOps workflows, Kubernetes environments, observability, security controls, and self-service tooling. Integrate and test autonomous hardware and software systems, translate requirements into specifications, coordinate subsystem testing, analyze results, and resolve interface and system issues. Collaborate with engineering, cybersecurity, IT, data science, vendors, and platform users while reviewing designs and mentoring engineers.

Manage contracts from capture and proposal through award and administration. Lead negotiations, ensure contractual and regulatory compliance, coordinate with internal functions and customer representatives, resolve contractual issues, support audits, finalize contract documentation, provide training, and compile contractual reports.

Coordinates earned-value management activities for Control Account Managers and Release-Train Engineers while supporting professional-services procurement and supply-chain operations. Responsibilities include supplier invoice validation, purchase order creation and maintenance, SOW administration, price analysis, reconciliation, supplier relationship management, advanced Excel and Tableau analysis, process documentation, and escalation of delivery risks. The role serves as a supply-chain subject-matter expert and collaborates across engineering, finance, program, and procurement teams.

Procurement internship for a currently enrolled undergraduate student. Responsibilities involve assignments within technical or engineering organizations, supporting supply chain activities through data analysis, reporting, prioritization, organization, and communication. The role requires proficiency with Microsoft Office, numerical and analytical skills, attention to detail, and the ability to work independently and collaboratively. U.S. citizenship is required for system access.

Designs system and subsystem architectures for CommandIQ battle management and command-and-control capabilities. Decomposes customer needs into roadmap features, defines and traces requirements, develops architectures using MBSE tools, documents interfaces, reviews systems engineering artifacts, and collaborates with cross-functional teams in an Agile/Scrum environment.

Develops, integrates, tests, and deploys AI, machine learning, autonomy, perception, planning, controls, and human-machine interface capabilities for mission systems. Translates requirements into production software, builds LLM and generative AI systems with agentic workflows, troubleshoots integrations, and supports the full development lifecycle. The role requires Python, React, TypeScript, cloud platforms, containerization, and software engineering practices.