Lead AI Engineer

BigBear.ai is hiring a Lead AI Engineer to direct the design, development, and deployment of generative AI and agentic systems for Department of War operations. The role sets AI architecture, leads and mentors engineers, and works with government stakeholders to deliver secure, production-ready AI capabilities.

Responsibilities:

• AI Architecture & Technical Leadership: Define and lead the architecture for generative AI solutions, including multi-agent workflows, retrieval-augmented generation (RAG), and LLM integration. Set engineering standards and best practices for building AI systems
• Agentic Systems & MCP Development: Design, build, and deploy multi-agent orchestration and custom Model Context Protocol (MCP) servers that connect AI agents securely to existing enterprise and mission systems
• LLM Integration & Optimization: Build applications against commercial and government-approved LLM endpoints (e.g., Anthropic, OpenAI, Ask Sage). Apply advanced prompt engineering, dataset routing, and confidence gating to optimize output quality and reliability
• Customer Discovery & Prototyping: Engage directly with government stakeholders to gather requirements, assess feasibility within time and cost constraints, and rapidly prototype solutions that prove mission value before scaling
• Full-Stack Development: Design, develop, document, test, and debug applications using full-stack principles across front-end, back-end, API, and data layers
• Cloud & Data Pipelines: Shape and implement cloud-forward data environments and web/data pipelines that feed AI workflows, including document ingestion, classification, and knowledge-domain routing
• AI Evaluation, Security & Assurance: Build evaluation harnesses for regression testing, citation verification, and retrieval transparency. Red-team AI systems for prompt injection and adversarial failure modes before production. Ensure solutions meet DoW security and compliance requirements (ATO, FedRAMP)
• Team Leadership & Mentorship: Lead and mentor engineers on AI tooling, AI-first development practices, and adoption strategies. Promote collaboration through initiatives such as hackathons and knowledge sharing
• Roadmap Influence: Feed field-discovered gaps and user feedback into product roadmaps, and work with product leadership on release planning and prioritization
• Deployment & Monitoring: Coordinate system installations, monitor production performance, and ensure deployed AI capabilities meet specifications and mission expectations