Job Description & Details
This is a heavy-duty backend and AI orchestration role focused on building autonomous agent workflows rather than just gluing APIs together. If you enjoy wrestling with multi-agent systems, state management, and turning raw LLM outputs into production-grade pipelines, this one is worth a serious look.
What You'll Actually Be Doing
Your day-to-day will revolve around designing and deploying the next generation of AI agentic frameworks to drive automated business operations. You won't just be calling OpenAI endpoints; you'll be engineering complex orchestration layers, building robust RAG retrieval systems, and setting up strict evaluation pipelines to ensure your agents don't hallucinate in production. Expect to collaborate heavily with product teams to translate vague workflow concepts into resilient, highly available backend architectures that won't fall apart under load.
The Core Tech Stack
You need a rock-solid foundation in Python and backend systems, paired with hands-on experience using orchestration frameworks like LangChain or LlamaIndex. They are heavily utilizing Generative AI services like OpenAI and Claude, so you need to intimately understand token limits, rate-limiting strategies, and how to structure context windows efficiently. Experience with vector databases and retrieval-augmented generation (RAG) is non-negotiable here because your primary job is connecting messy enterprise data layers directly to execution engines.
Interview Expectations
Expect the hiring team to test your system design chops by asking you to architect a multi-agent workflow that handles concurrency, retries, and state persistence when an LLM call fails mid-task. They are secretly looking to see if you understand the actual failure modes of distributed AI systems or if you just know how to write a simple script that calls an API. They will also likely drill down into how you manage chunking strategies and embedding models for RAG, so be prepared to defend your choices on chunk sizes and retrieval latency.
Application Advice
Skip the generic AI buzzwords and make sure your resume immediately highlights production backend experience alongside concrete examples of deployed LLM integrations. Explicitly mention terms like multi-agent orchestration, RAG pipelines, API integrations, and system scalability so you easily clear their initial ATS filters. Show, don't just tell, how you've handled asynchronous processing and state management in past Python-heavy backend roles.