ML Engineer, Retrieval & Grounded Generation

RESILIENCE IN THE FACE OF DISRUPTION. DEFCON AI is an insights company that leverages artificial intelligence, mathematical optimization, data analytics, and software engineering for resilient optimization of complex systems.

In today's dynamically changing world, DEFCON AI's technology aligns outcomes with operational goals, better decision making, and empowers customers to anticipate assess, and mitigate the impacts of disruptions.

You'll join the analytics and AI engineering team behind a system that genuinely matters: an AI-assisted platform that pulls together records from dozens of external feeds, resolves them to the right person, surfaces what a human reviewer should look at first, and explains every recommendation in plain, defensible terms - running inside a secure government cloud environment. It's the kind of problem where the details you get right are the ones that count, which is exactly what makes it worth doing well.

As ML Engineer, Retrieval & Grounded Generation, you'll build embeddings, vector storage, and retrieval at scale, and integrate language models so that every piece of generated text is bound to cited source records and citation failures are tested for rather than assumed away. You'll also own prompt and output-schema design; model packaging, versioning, serving, and rollback; and the telemetry hooks that make later measurement possible without manual reconstruction - real infrastructure for a real production system, not a demo.

This is a fully remote role, with occasional travel to DEFCON AI HQ, customer sites, and vendor facilities as required.

• Implement embeddings, vector storage, and retrieval across a large, provenance-tracked evidence base
• Integrate language models so generated text is bound to cited source records; test for citation failures rather than assuming them away
• Design prompts and output schemas
• Own model packaging, versioning, serving, and rollback
• Instrument telemetry for retrieval and generation quality, recommendation/version attribution, overrides, abstentions, grounding failures, latency, throughput, and measurement events defined with Model Test
• Provide bounded model assistance for difficult narrative extraction where deterministic processing is insufficient, with every output tied to its source passage
• Supply the recorded rule context to every model-assisted step, so each output carries the exact rule versions and ordered context it received
• Maintain a modular in-boundary serving path, self-hosted or managed, alongside the primary managed inference service, so the platform does not depend on one provider's availability or approval

• 5+ years of experience, including a production or near-production retrieval-augmented (RAG) system you built yourself
• Ability to speak in detail to your retrieval design, which vector store you used and why, how you tested grounding, what citation failures looked like in practice, and how rollback worked
• Strong Python, with hands-on experience in embeddings and vector retrieval at scale
• Clarity on what actually shipped in past work — prototype, proposal, or deployed code — since that distinction matters more here than the title on a resume
• US Citizenship Required
• Active US Secret clearance required to start.

• Experience deploying models into restricted or air-gapped environments
• Self-hosted or open-weight model operation
• Fine-tuning, adapters, or custom embeddings
• Federal Dev Sec Ops , RMF, ATO, or DoW cloud environment experience
• Active Top Secret clearance

• Generated explanations that assert no more than the sources support, with the citation path intact and citation failures tested rather than assumed…