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Adelphi Data is building the largest AI deployment company for national security. The company has grown 10x in under two years and works directly with senior leaders at the Department of War and Intelligence Community.
As a Machine Learning Engineer, you will contribute to end-to-end delivery of agentic, full-stack systems built on frontier models, from prototype to stable production. You'll work embedded alongside defense and intelligence customers, solving core bottlenecks in warfighting, intelligence operations, and enterprise systems.
Key responsibilities include:
- Contributing across the full LLM stack: OS, storage, network, API, and transport layers
- Building and deploying ML services leveraging LLMs, embeddings, RAG, and agent orchestration into production environments, including classified and air-gapped systems
- Working directly with customers to understand problems, support delivery sequencing, and ship AI applications under real-world constraints
- Codifying repeatable patterns into reusable tools and building blocks to accelerate team velocity
- Delivering solutions that turn frontier-model capability into mission outcomes
You are an AI-native engineer who leverages LLMs and AI tooling daily as core design and development practices. You use AI coding tools (Claude Code, Cursor, Copilot) instinctively and stay current with emerging models. You have working knowledge of modern agent frameworks (LangGraph, OpenAI Agents SDK, Claude Agent SDK, AutoGen) and familiarity with MCP or similar LLM integration frameworks. You maintain a clear-eyed view of AI limitations and know when to trust versus verify AI-generated output.
Bonus experience includes infrastructure management (Docker, Kubernetes, AWS), encryption, authentication, Linux systems administration, DevOps/SRE, production agentic services, customer-facing embedded delivery, and federated or privacy-preserving data architectures.
An active U.S. Government security clearance is strongly preferred; clearance-eligible candidates are welcome. The role is based in the DC Metro area; remote candidates will be considered with 25% travel expected.
