Note: The job is a remote job and is open to candidates in USA. General Dynamics Mission Systems engineers high-technology solutions, products, and services for defense and scientific missions across multiple operational domains. The Artificial Intelligence Software Engineer will build and deploy production AI services, including agentic workflows, RAG pipelines, LLM applications, data-to-insight pipelines, secure API integrations, and reliable containerized deployments for enterprise systems.
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
- Platform architecture decisions — that's the Lead Architect's job
- Project coordination, scheduling, or status reporting
- Vendor evaluations or tool selection committees
Skills
- Bachelor's degree in Software Engineering, or related Science, Technology, Engineering or Mathematics field, plus a minimum of 8 years of relevant experience; or Master's degree, plus 6 years relevant experience
- Ability to obtain a Department of Defense Secret security clearance is required at time of hire
- Due to the nature of work performed within our facilities, U.S. citizenship is required
- • 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
- • 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
Benefits
- Remote — 100% telework
- 9/80 schedule
- Flexible work environment
- Contributions are recognized and rewarded
Company Overview