AI Engineer

• Design reliable AI workflows: Build multi-step, tool-using agents with typed tool contracts, deterministic routing, self-checks, and guardrails
• Context engineering: Implement selection, compression, isolation, structured memory, query planning, and retrieval strategies, with metrics and debuggability
• Orchestration & correctness: Own state machines/graphs for agent steps; backoffs/retries; idempotency
• Interoperability: Add adapters to invoke external agents and let our capabilities be invoked on the other end, (e.g., headless/MCP); enforce typed contracts and least-privilege context across boundaries
• Compute in the loop: Orchestrate computational/AI models (simulation, optimization, classic ML, foundation models) alongside LLM steps; schedule long-running jobs with provenance, caching, and resumability
• Ship production systems end-to-end: Stand up services, write clean REST/gRPC APIs, wire queues/workers, add feature flags, and harden for scale
• Cost, latency, quality: Optimize context budgets, caching, model/tool selection, track and improve SLOs
• Make AI observable & safe: Instrument traces/metrics/logs across LLM calls, tools, retrieval, and compute; build offline/CI evaluation harnesses (faithfulness, retrieval quality, safety, task success) that gate deploys.

AI Engineer

Senior Machine Learning Scientist – Search, Recommendations, AI Engineering

Lead AI Engineer

Lead Full-Stack GenAI Platform Software Engineer

Senior Manager – Data and AI Platform Activation

AI Platform Engineer