Job description

Standard Job Description Join our team as a Senior AI Engineer focused on ontologies, knowledge graphs, and production AI systems. You will turn complex enterprise data into trusted knowledge that AI applications can understand and use. You will work directly with users, domain experts, data owners, and platform teams. This role uses a forward-deployed engineering model. You will identify user problems, build solutions, deploy them, and improve them through direct feedback. You will own work from early discovery through production support. You must be comfortable with unclear requirements, unfamiliar data, and complex system constraints. Responsibilities include: • Work with users and domain experts to define problems, outcomes, and technical requirements. • Convert enterprise concepts and source metadata into clear ontologies and semantic models. • Design knowledge graphs that connect business meaning to source data. • Build AI agent workflows that create, evaluate, and improve ontology artifacts. • Develop evaluation methods for AI quality, source traceability, query accuracy, and human review. • Integrate relational databases, APIs, files, cloud platforms, and enterprise data systems. • Build production software across services, APIs, background jobs, web applications, and graph query systems. • Create tests and controls for AI output, ontology quality, data mappings, and production behavior. • Deploy software through automated pipelines, containers, and cloud platforms. • Observe production systems and resolve failures across application, AI, data, and infrastructure components. • Make clear tradeoffs among user value, technical quality, security, schedule, and long-term support. • Present technical decisions to engineering teams, domain experts, and business leaders. • Review designs and code, mentor other engineers, and improve team engineering practices. • Apply lessons from each user engagement to reusable platform capabilities. This position is for a senior individual contributor. The engineer will lead technical work without requiring formal personnel authority. Basic Qualifications • A bachelor’s degree in Computer Science, Engineering, Data Science, Applied Mathematics, or a related STEM field, with five years of relevant experience. • A master’s degree with three years of relevant experience can satisfy the education and experience requirement. • Experience developing production software in Python, including services, APIs, data pipelines, or distributed applications. • Experience building and deploying AI, machine learning, LLM, or agent-based systems beyond an initial prototype. • Experience designing ontologies or knowledge graphs with RDF, OWL, and SPARQL. • Experience working directly with users or domain experts to convert unclear needs into deployed software. Desired Skills • Experience with SHACL, R2RML, ontology reasoning, ontology governance, or competency-question evaluation. • Experience with graph databases or virtual knowledge graph platforms, such as Amazon Neptune or Ontop. • Experience with AI agent frameworks and model platforms, such as Strands Agents, AWS Bedrock, or Azure OpenAI. • Experience evaluating LLM systems for accuracy, traceability, safety, reliability, cost, and latency. • Experience with Django, REST APIs, asynchronous Python, Celery, PostgreSQL, or Redis. • Experience with React, TypeScript, or full-stack application development. • Experience connecting SQL databases, REST APIs, OpenAPI specifications, files, or Databricks. • Experience with Docker, Kubernetes, OpenShift, Helm, and CI/CD pipelines. • Experience with production observability, structured logs, metrics, tracing, and incident resolution. • Experience applying security and access controls to enterprise AI or data systems. • Experience with ontology editors, semantic modeling tools, or graph visualization tools. • Experience mentoring engineers or leading technical work across multiple teams. • Experience delivering software in a customer environment with changing requirements and limited information. 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): $122600.00 - $227600.00 • 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): $110300.00 - $204900.00 • Includes metropolitan areas such as Denver, CO; King of Prussia, PA; Stratford, CT; Moorestown, NJ; etc. • Full-time salary range (GEOZONE 3): $98100.00 - $182100.00 • Includes metropolitan areas such as Dallas–Fort Worth, TX; Orlando, FL; Grand Prairie, TX; Marietta, GA; etc. • Full-time salary range (GEOZONE 4): $88300.00 - $163900.00 • 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.

Requirements

Must-haves

Standard Job Description
Join our team as a Senior AI Engineer focused on ontologies, knowledge graphs, and production AI systems.
You will turn complex enterprise data into trusted knowledge that AI applications can understand and use. You will work directly with users, domain experts, data owners, and platform teams.
This role uses a forward-deployed engineering model. You will identify user problems, build solutions, deploy them, and improve them through direct feedback.
You will own work from early discovery through production support. You must be comfortable with unclear requirements, unfamiliar data, and complex system constraints.
Work with users and domain experts to define problems, outcomes, and technical requirements.
Convert enterprise concepts and source metadata into clear ontologies and semantic models.
Design knowledge graphs that connect business meaning to source data.
Build AI agent workflows that create, evaluate, and improve ontology artifacts.
Develop evaluation methods for AI quality, source traceability, query accuracy, and human review.
Integrate relational databases, APIs, files, cloud platforms, and enterprise data systems.
Build production software across services, APIs, background jobs, web applications, and graph query systems.

Nice-to-haves

Experience with SHACL, R2RML, ontology reasoning, ontology governance, or competency-question evaluation.
Experience with graph databases or virtual knowledge graph platforms, such as Amazon Neptune or Ontop.
Experience with AI agent frameworks and model platforms, such as Strands Agents, AWS Bedrock, or Azure OpenAI.
Experience evaluating LLM systems for accuracy, traceability, safety, reliability, cost, and latency.
Experience with Django, REST APIs, asynchronous Python, Celery, PostgreSQL, or Redis.
Experience with React, TypeScript, or full-stack application development.
Experience connecting SQL databases, REST APIs, OpenAPI specifications, files, or Databricks.
Experience with Docker, Kubernetes, OpenShift, Helm, and CI/CD pipelines.
Experience with production observability, structured logs, metrics, tracing, and incident resolution.
Experience applying security and access controls to enterprise AI or data systems.
Experience with ontology editors, semantic modeling tools, or graph visualization tools.
Experience mentoring engineers or leading technical work across multiple teams.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aipythonmachine learningllmawsazurerestreacttypescriptsqldockerkubernetesci/cd

Platform: eightfold
Effort: medium

Benefits

Benefits detected in the listing.

Dental

Vision

401(K)

Pto

Paid Time Off

Parental Leave