Role overview
- Hands-on experience building LLM-based applications in Python, including structured prompting and multi-step workflows.
- Practical experience with at least one agent framework or orchestration approach (e.g., LangChain or a comparable framework). Experience with LangGraph is a plus.
- Working knowledge of prompt templates and basic memory/state concepts (e.g., session memory, conversation state, lightweight persistence).
- RAG fundamentals with some implementation exposure: ability to build a basic retrieval flow (data chunking embeddings retrieval grounded answer) and iterate on quality.
- Debugging mindset for agentic workflows: comfortable troubleshooting tool-calls, retrieval misses, and prompt/chain issues; familiar with logs/traces or basic observability practices.
- Strong English language knowledge.
What you'll work on
- Design and implement agent‑to‑agent communication patterns and multi‑agent collaboration workflows.
- Integrate LLMs, RAG pipelines, and external APIs/tools into end‑to‑end agent systems.
- Develop, test, and iterate on orchestration logic (routing, planning, tool selection, error handling, and structured execution).
- Build prompt templates, memory/state management, and reusable workflow components to improve reliability and consistency.
- Implement and optimize RAG + embedding workflows (retrieval strategy, chunking, evaluation, iteration).
- Own testing & debugging for multi‑agent systems, including tracing, evaluation, and regression testing across workflows.
What we're looking for
- LangGraph experience (graph/state-machine orchestration) and/or multi-agent patterns.
- Langfuse (or similar tracing/evaluation tooling) for prompt/version tracking, traces, and quality monitoring.
- Experience improving RAG quality beyond the basics (evaluation, retrieval strategies, chunking experiments).
- Exposure to testing practices for LLM apps (lightweight evaluation, regression checks, reproducibility habits).
Tags & focus areas
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