AI Engineer - Senior

About the role

Most of this work is not about the model-it is about everything around it: getting an agent to reach real systems safely, recover from tool failures, respond fast, and run cost-effectively. You will take full ownership of client systems, making key architectural calls and building high-reliability backend services.

Key focus areas include:

• Designing request classification, model planning, tool permissions, and multi-turn state management.
• Connecting agents to internal APIs, databases, and third-party tools via typed, versioned contracts, using Python async and streaming (SSE/WebSockets).
• Building golden datasets, offline scoring, traces, and online experiments-ensuring performance is driven by metrics (p95 latency, cost per task, failure rates), not intuition.
• Enforcing scoped permissions, confirmation gates for irreversible actions, and defense against prompt injection attempts.

• Extensive Python backend experience, with async as an everyday tool and strong experience designing service contracts.
• Proven track record taking at least one LLM system to production and maintaining it.
• Hands-on work with tool calling, multi-step workflows, and handling failure paths (timeouts, retries, bad arguments, hallucinated parameters).
• Standard reliability engineering applied to non-deterministic runtimes: circuit breakers, backoff strategies, caching, and concurrency limits.
• Discipline to build systematic measurement layers (evals, tracing) before deploying features.

• Production experience with streaming architectures (SSE/WebSockets) and frontend contract design.
• Hands-on use of agent frameworks (LangGraph, LangChain) or protocols like Model Context Protocol (MCP).
• Experience with LLM tracing/eval tools, vector databases, hybrid search, and chunking/ingestion pipelines.
• Quantifiable success in reducing LLM latency or token costs.
• Core backend stack: PostgreSQL, Redis, Docker, AWS, CI/CD, OpenTelemetry, pytest.