Applied AI Engineer

Lumbra builds Nebula, an agentic AI platform for analysts in the Intelligence Community and Department of Defense. The Applied AI Engineer develops, evaluates, and ships production AI features, including agents, retrieval, tool integrations, and execution and evaluation infrastructure.

Responsibilities:

• ~60% building. Agent behaviors, retrieval pipelines, tool integrations, and the product surfaces around them, shipped to production
• ~25% evaluating and iterating. Eval sets, graders, regression suites, and failure analysis that tell you whether a change made a feature better
• ~15% closing the loop. Working with Forward Deployed Engineers and Deployment Strategists to turn feedback from classified deployments into fixes
• AI features end to end, from prototype to production: agent behaviors, retrieval, tool use, and the interfaces analysts touch
• Evaluation for everything you ship. Datasets, graders, and regression suites that catch capability loss without overfitting to specific outputs
• Evidence-driven iteration on prompts, context, retrieval, and tooling, driven by failure analysis rather than intuition
• Production quality for model calls: latency, cost, error handling, fallbacks, and observability
• Vendor neutrality. Features that hold up across model providers, including the models available on classified networks
• The feedback loop with the field. Field learnings come back weekly, and you turn them into product
• The agent loop and execution runtime. Build and scale the foundation that sends requests to models, manages tool execution and long-running sessions, and feeds results back into the model’s context
• Evaluation harnesses. Automated benchmarking pipelines and eval suites that stress-test accuracy, latency, context-window limits, and regressions
• Retrieval and orchestration architecture. Reliable RAG frameworks and orchestration layers that carry high-throughput, low-latency AI features