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.
Standard Job Description
Join our team as a Staff AI Engineer focused on ontologies, knowledge graphs, and production AI systems. You will define how complex enterprise data becomes trusted knowledge that AI applications can understand and use.
This role uses a forward-deployed engineering model. You will work directly with users, domain experts, data owners, and engineering teams. You will identify high-value problems and turn unclear needs into secure, reliable production capabilities. You will remain responsible from initial discovery through deployment and operation.
As a Staff individual contributor, you will set technical direction across projects and teams. You will solve complex system problems, guide senior engineers, and create reusable platform capabilities. You will lead through technical judgment, delivery results, and trusted relationships. Formal personnel authority is not required.
Responsibilities include:
- Lead technical discovery with users, domain experts, data owners, and engineering teams.
- Convert business needs into system requirements, architecture decisions, and delivery plans.
- Define architectures for AI agents, ontologies, knowledge graphs, data integrations, and user applications.
- Design semantic models that represent business concepts instead of physical source structures.
- Establish methods for ontology governance, source traceability, evaluation, version control, and human review.
- Build AI agent workflows that create, test, and improve trusted knowledge artifacts.
- Define evaluation methods for AI quality, semantic accuracy, query results, reliability, cost, and latency.
- Integrate databases, APIs, files, cloud platforms, and enterprise data systems.
- Lead development across services, APIs, background jobs, web applications, and graph query systems.
- Guide deployment through automated pipelines, containers, and cloud platforms.
- Resolve complex failures across AI models, applications, data sources, and infrastructure.
- Make tradeoffs among user value, security, quality, schedule, cost, and long-term support.
- Convert engagement-specific solutions into reusable platform capabilities.
- Review architecture and code for quality, security, supportability, and system fit.
- Mentor engineers and establish consistent engineering practices across teams.
- Present technical decisions, risks, and results to technical and business leaders.
- Influence technical direction across teams without formal authority.
Basic Qualifications
- A bachelor’s degree in a related STEM field and nine years of relevant experience.
- A master’s degree with seven years or a PhD with four years can satisfy the education and experience requirement.
- Experience developing production software in Python and architecting distributed or cloud-based systems.
- Experience architecting and deploying production AI, LLM, machine learning, or agent-based systems.
- Experience designing ontologies or knowledge graphs with RDF, OWL, and SPARQL.
- Experience leading technical work across teams and converting unclear user needs into deployed solutions.
Desired Skills
- Advanced experience with OWL, SHACL, R2RML, SPARQL, ontology reasoning, or semantic validation.
- Experience defining ontology governance, model reuse, version control, and review practices.
- Experience with graph databases or virtual knowledge graph systems.
- Experience with Amazon Neptune, Ontop, RDFLib, or comparable semantic platforms.
- Experience with AI agent frameworks and enterprise model platforms.
- Experience with AWS Bedrock, Azure OpenAI, or comparable model services.
- Experience evaluating LLM systems for accuracy, traceability, reliability, security, cost, and latency.
- Experience designing human review and approval controls for AI-generated artifacts.
- Experience with Django, asynchronous Python, Celery, PostgreSQL, Redis, or comparable technologies.
- Experience with React, TypeScript, or full-stack application development.
- Experience integrating SQL databases, REST APIs, OpenAPI specifications, files, or Databricks.
- Experience with Docker, Kubernetes, OpenShift, Helm, and CI/CD pipelines.
- Experience with production observability, incident response, and root-cause analysis.
- Experience applying security and access controls to enterprise AI or data systems.
- Experience leading architecture reviews or technical standards across engineering teams.
- Experience mentoring senior engineers and improving team engineering practices.
- Experience delivering software in customer environments with unclear or changing requirements.
- Experience converting customer-specific solutions into reusable products or platform services.
Pay Information
GeoZone Definition: GeoZones are geographic groupings created by Lockheed Martin to align compensation ranges with regional labor markets and cost-of-labor differences across the United States. Locations are assigned a Geo Zone based on the primary work location of the role.
- Full-time salary range (GEOZONE 1): $150800.00 - $280000.00
- Includes metropolitan areas such as Sunnyvale CA; Pal Alto, CA; New York City metropolitan area; Newark, New Jersey; etc.
- Full-time salary range (GEOZONE 2): $135700.00 - $251900.00
- Includes metropolitan areas such as Denver, CO; King of Prussia, PA; Stratford, CT; Moorestown, NJ; etc.
- Full-time salary range (GEOZONE 3): $120600.00 - $224000.00
- Includes metropolitan areas such as Dallas–Fort Worth, TX; Orlando, FL; Grand Prairie, TX; Marietta, GA; etc.
- Full-time salary range (GEOZONE 4): $108600.00 - $201600.00
- Includes metropolitan areas such as Camden, AR; Lexington, KY; Lufkin, TX; etc.
At Lockheed Martin, we know mission success starts with taking care of our people. Our Total Rewards program is designed to attract top talent, support your well-being, and help you grow—both professionally and personally.
The salary range for this position is as listed on the requisition. Please note that the salary information listed is a general guideline only.
Lockheed Martin considers factors such as (but not limited to) scope and responsibilities of the position, candidate's work experience, education/ training, key skills as well as market(work location) and business considerations when extending an offer.
Benefits offered: Medical, Dental, Vision, Flexible work arrangements and schedules (e.g., 4x10), 401(k) match, Paid time off, Holidays, Parental Leave, EAP, Flexible Spending Accounts, Education Assistance, Life Insurance, Short-Term Disability, and Long-Term Disability.
- Annual short-term and/or long-term incentive compensation programs may be offered depending on the position. Payments under these annual programs are not guaranteed and can vary from year to year and are tied to a range of performance metrics.
- For (Washington state applicants only) Non-represented full-time employees: accrue at least 10 hours per month of Paid Time Off (PTO) to be used for incidental absences and other reasons; receive at least 90 hours for holidays. Represented full time employees accrue 6.67 hours of Vacation per month; accrue up to 52 hours of sick leave annually; receive at least 96 hours for holidays. PTO, Vacation, sick leave, and holiday hours are prorated based on start date during the calendar year.
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