Machine Learning Engineer

Requirement Details Backend/Systems Experience 3+ years building production backend or distributed systems (pre-AI experience required) Production AI Systems Has shipped AI/LLM features serving real users at scale — not just prototypes or demos Agentic Systems Has built AI agents, skills, tools, or MCP (Model Context Protocol) integrations Python Proficient for backend development Secondary Language Working knowledge of Go, TypeScript, or Rust Cloud Infrastructure Deep experience with AWS/GCP/Azure — cost optimization, compute decisions, not just deployment Container & Orchestration Hands-on with Docker and Kubernetes — can build, deploy, debug, and scale services themselves LLM Integration Understands token economics, context limits, rate limiting, structured outputs, API failure modes LLM Evaluation Understands how to evaluate LLM outputs and the inherent challenges (non-determinism, quality measurement, regression detection) Hands-On Engineer Not just an architect — writes code, debugs production issues, deploys their own work \_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_ Preferred / Differentiators • Built multi-step agentic workflows with tool use and function calling • Experience with agent orchestration frameworks (LangGraph, CrewAI, or custom) • Built guardrails, fallbacks, or graceful degradation for AI systems • Streaming inference and async agent orchestration • Cost/latency optimization: caching, batching, prompt compression • ML observability tools: Langfuse, Arize, Braintrust, W&B • Retrieval systems (vector search, hybrid search) — as a tool, not the focus