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Remote, mid-level AI title increases applicant density, but niche LLM agent requirements temper competition.
LLM engineering and agent orchestration skills transfer easily across industries.
Multiple mandatory LLM, LangGraph, production, and cloud requirements create high shortlisting strictness.
Design and own the AI agent roadmap: identify, prioritize, and build AI-powered agents to automate complex tasks across sales, data, product, and other teams.
Hands-on development of AI agents including architecture design, coding, debugging, and platform development (shared tool layers, memory, tracing, evaluation harness).
Ensure production-level reliability and efficiency of AI solutions, including cost, latency, quality metrics, multi-turn interactions, and evaluation frameworks.
6+ years of production software development experience; at least 2 years specifically working on LLM systems with real users.
Strong practical experience with agentic AI systems: ReAct loops, tool/function calling, planners, state management, long-term memory, HITL steps, and streaming.
Proficiency with Python and related frameworks (FastAPI, async job processing), plus cloud production experience with AWS or equivalent (including Docker and CI/CD).
Experience with LangGraph or similar agent orchestration tools, and observability stacks like LangSmith or Langfuse.
Technically creative thinker who can identify impactful AI agent use cases from business contexts, balancing feasibility and impact.
Experienced architect who can design scalable, maintainable agent platforms focused on long-term reliability, modularity, and measurable evaluation.
Highly hands-on engineer with deep production experience delivering and iterating AI agent code at pace, able to critique and improve existing architectures.