What You'll Own

• Production AI services. Build and deploy agentic workflows, RAG pipelines, and LLM-integrated applications using Python, LangChain/LangGraph, and commercial foundation models (Claude, Codex, Gemini, open source).
• Data-to-insight pipelines. Implement document ingestion workflows that transform unstructured enterprise data into structured models for AI reasoning — embeddings, vectorization, knowledge graphs.
• API integration. Design and build secure API interfaces that connect AI services to internal tools, enterprise platforms (Oracle, IFS, Snowflake, PLM, MES, CRM), and data sources.
• Deployment and reliability. Containerize and deploy AI services using Docker and Kubernetes. Build monitoring and evaluation pipelines to track model reliability, latency, and operational performance.
• Prompt engineering at scale. Design, test, and optimize prompts and agent configurations for production use — not demos.

What You Won't Own

• Platform architecture decisions — that's the Lead Architect's job
• Project coordination, scheduling, or status reporting
• Vendor evaluations or tool selection committees

What Makes This Role Different

• You will build production AI systems for a 10,000-person enterprise — not prototypes, not demos, not proofs of concept
• You will work in a multi-model environment (Claude, Codex, Gemini, open source) on real enterprise problems — legacy modernization, ERP replacement, manufacturing intelligence
• AI-assisted development is the default workflow — Claude Code, Codex, agentic tooling. You will use AI to build AI.
• The team is small, the problems are hard, and your code ships to production. Your work will directly change how a major defense enterprise operates.

Required Qualifications

• Bachelor’s degree in Computer Science, Software Engineering, or a related field, plus 5 years of experience; or Master’s degree plus 3 years of experience
• Production experience building applications with LLM APIs — you have deployed generative AI services that real users relied on, not just experimented with in notebooks
• Strong Python development skills — you write clean, testable, production-grade code, not scripts
• Experience with RAG pipelines, vector databases, and document ingestion workflows in production environments
• Experience building and consuming REST APIs — you have integrated AI services with enterprise systems and data platforms
• Containerized deployment experience — Docker, Kubernetes, CI/CD pipelines. You have shipped code through automated pipelines, not manual deployments.
• U.S. citizenship required. Department of Defense Secret security clearance is required at time of hire.

Preferred Qualifications

• Experience with agent frameworks — LangChain, LangGraph, or similar tools for building multi-step, tool-using AI workflows
• Experience with multiple cloud platforms (AWS, Azure, GCP) including cloud-native AI services
• Hands-on use of AI-assisted development tools (Claude Code, GitHub Copilot, Cursor) as part of your daily workflow
• Experience with streaming data pipelines (Kafka, Airflow) and production data infrastructure
• Model monitoring and evaluation — you have built systems to track AI service reliability, not just accuracy metrics in a notebook
• Commercial technology background — SaaS, healthcare, fintech, or platform engineering. Defense experience is not required.

What Sets You Apart

• You build things that work. Your default response to a problem is code, not a document.
• You have shipped AI systems that real users depended on in production.
• You are comfortable working without detailed specs — you can take a problem statement and figure out the right approach.
• You care about reliability as much as capability — you monitor what you deploy.
• You move fast without being reckless. You know when to iterate and when to get it right the first time.

Details

• Remote — 100% telework
• 9/80 schedule
• Defense industry experience is not required