Artificial Intelligence (AI) Engineer

General Dynamics Mission Systems, Inc•,

23h•$142,696 - $158,303•Remote

About The Position

This role involves building production AI systems for a 10,000-person enterprise, focusing on real enterprise problems within the defense sector. The work includes developing agentic workflows, RAG pipelines, and LLM-integrated applications using Python and various foundation models. AI-assisted development is the default workflow, meaning AI will be used to build AI. The team is small, the problems are challenging, and the code ships directly to production, impacting how a major defense enterprise operates.

Requirements

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.
S. citizenship required. Department of Defense Secret security clearance is required at time of hire.

Nice To Haves

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.

Responsibilities

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.