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Remote and mid-level amplify competition, specialized LLM niche moderates it.
Highly domain-specific LLM agent, observability, and orchestration skills limit cross-industry transferability.
Multiple explicit years and mandatory specialized LLM tooling and backend stack make filters highly stringent.
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Build and evolve production AI agents on foundation models including orchestration and context engineering.
Develop and maintain evaluation pipelines, observability, and monitoring for production LLM and agent systems.
Collaborate as the first dedicated AI-agent engineer within a small product team, delivering measurable outputs from onboarding.
At least 2 years experience building production LLM agents with tool-calling loops, streaming, context management, and orchestration.
Production experience with agent frameworks such as LangGraph, LangChain, or custom orchestration.
Minimum 1.5 years experience in evaluation-driven development including datasets and pipelines covering tool selection and LLM-as-judge.
5+ years backend engineering experience with TypeScript/Node, Postgres, and serverless AWS.
Experienced in shipping agentic AI systems to production with hands-on knowledge of failure modes and mitigations across the AI stack.
Strong agent-architecture fundamentals with proven delivery of AI/agent engineering projects on roadmap timelines.
Direct experience with LLM observability toolsets like Langfuse or OpenTelemetry and cost optimization techniques such as prompt caching and model routing.