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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Agentic Ontology Software Engineer - **Company:** Lockheed Martin - **Location:** Stateline, NV, United States - **Experience:** Expert - **Salary:** $122,600.0 - $227,600.0 - **Contract:** Permanent contract - **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Amazon Web Services, Microsoft Azure, Cloud Computing, Code Review, Computer Programming, Continuous Delivery, Continuous Integration, Data Systems, Data Visualization, Relational Databases, Distributed Systems, Django Web Framework, Graph Database, Hardware Virtualization, Python (Programming Language), PostgreSQL, Machine Learning, Metadata, OpenShift, Resource Description Framework (RDF), Redis, Software Engineering, SPARQL, SQL Databases, TypeScript, Web Applications, Openapi, Enterprise Data Management, Scripting, ReactJS, Delivery Pipeline, Large Language Models, Kubernetes, Information Technology, Data Management, Celery, Virtual Agents, Restful APIs, Data Pipelines, Docker, Databricks - **Published:** September 30, 2026 - **Apply:** https://www.careerbuilder.com/job-details/agentic-ontology-software-engineer-stateline-nv--a9e638c3-b19f-4675-b529-f6d5f071f0c2 ## About the Role * 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., Access Control, Amazon Web Services (AWS), Application Programming Interface (API), Artificial Intelligence (AI), Borland ObjectWindows Library (OWL) Programming Libraries, Cloud Computing, Code Reviews, Computer Science, Continuous Deployment/Delivery, Continuous Integration, Data Management, Data Science, Distributed Applications, Django, Docker, Hardware Virtualization, Identify Issues, Internet Application, Machine Learning, Mathematics, Mentoring, Metadata, Metrics, Microsoft Windows Azure, Ontology, Performance Metrics, PostgreSQL, Production Support, Production Systems, Prototyping, Python Programming/Scripting Language, RDF (Resource Description Framework), REST (Representational State Transfer), React.js, Redis, Relational Databases (RDBMS), SPARQL, SQL Databases, Software Development, Software Engineering, Technical Leadership, Technical Presentation, Test Plan/Schedule, Traceability ## Description 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. 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