Applied AI Engineer

Turn AI models into reliable product behavior. Build systems that measure, stress-test, and continuously improve outputs. Make it fast, cost-efficient, and controlled. Ensure users can understand, trust, and steer AI decisions.

• Experience building evaluation-driven AI systems with continuous monitoring and iteration loops.
• Familiarity with AI safety, governance, and control mechanisms in production environments.

• Design and implement AI systems including RAG, agents, routing strategies, and model selection pipelines.
• Build evaluation frameworks, datasets, and feedback loops to measure real-world performance and guide iteration.
• Harden AI systems against failure modes such as prompt injection, hallucinations, and unsafe tool execution.
• Optimize latency, cost, and quality through caching, batching, retrieval improvements, and model orchestration.

• Strong applied experience with LLM systems, including prompt design, retrieval, or agent-based workflows.
• Ability to build production-grade systems that handle uncertainty, variability, and imperfect model behavior.
• Experience evaluating model performance and making principled tradeoffs between quality, cost, and speed.
• Comfort working across product, engineering, and research boundaries to ship real-world AI features.