Lead AI Product Engineer - AI Agents & Emerging Products

Strong experience building LLM-powered systems in production, including prompt engineering, structured outputs, tool/function calling, and agentic coding workflows where agents write, modify, and validate code within guardrails. Solid backend engineering skills with Python and/or Node.js, designing APIs, async services, and background workers that support long-running, stateful agent workflows. Experience designing multi-step, agent-driven execution loops (plan → act → evaluate → retry), with explicit state management, idempotency, and failure recovery. Comfort with distributed systems primitives (queues, pub/sub, durable state, rate limiting, concurrency controls) required to coordinate agents at scale. Proven ability to instrument and observe agent behavior, emitting structured events for task success, failures, retries, overrides, and code-generation outcomes. Experience deploying and operating systems on cloud infrastructure (AWS or GCP), using Docker and CI/CD pipelines. Familiarity with inter-agent communication patterns, including message schemas, shared memory, blackboard systems, or event-driven coordination. Experience implementing agent evaluation pipelines (prompt regression tests, simulation-based evals, automated scoring, human-in-the-loop review). Understanding of cost-aware execution for agents (dynamic model routing, caching, batching, budget constraints). Experience with RAG architectures, vector databases, and persistent memory strategies. Exposure to emerging agent protocols (e.g., x402 payment flows, MCP), with the ability to implement evolving specs pragmatically.