This role focused on building and operating multi-agent AI applications, RAG systems, and production workflows. The engineer owns features end-to-end - from agent design and retrieval pipelines to APIs, UI, and deployment - in a Dockerized, enterprise product environment.
Key Responsibilities
Design, build, and iterate on multi-agent systems (orchestration, tool calling, memory, handoffs, evaluation).
Build and improve RAG pipelines: ingestion, chunking, embeddings, hybrid search, reranking, grounding, and citation quality.
Implement workflow engines for long-running business processes (state machines, async jobs, retries, human-in-the-loop).
Develop backend services in Python (APIs, services, background workers).
Own DevOps for these systems: containers, CI/CD, secrets, observability, and cost/latency controls.
Work with product and domain experts to turn ambiguous requirements into reliable agent behavior.
Write tests, evals, and runbooks so agent systems stay shippable in production.
Qualifications & Requirements
Core Requirements:
Experience: 4–7+ years of professional software engineering experience, with dedicated hands-on experience building and shipping AI applications.
Education: Bachelor's degree in Computer Science, Engineering, or equivalent practical experience, backed by a portfolio of shipped multi-agent or RAG projects.
Backend: Strong production experience with Python, including REST/GraphQL APIs, microservices, and async/background workers (Celery/Redis task queues).
Multi-Agent Systems: Hands-on experience with agent frameworks (e.g., LangGraph, LangChain), tool calling, and multi-agent orchestration patterns.
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