Techmatters - AI Engineer - LLM/RAG (3-7 yrs)

Responsibilities :

- Built and shipped multi-agent systems in production, not prototypes, not demos. Real systems with real failure modes.

- Worked with LangGraph, LangChain, CrewAI, AutoGen, or equivalent orchestration frameworks and can explain why you made that choice.

- Designed and queried knowledge graphs or graph databases like Neo4j or graph layers on relational systems. You understand why a graph is the right data model for relationship-heavy problems and not just because it looks cool.

- Built systems that detect absence, not just what's wrong but what's missing. This is a specific reasoning skill and we'll test for it.

- Written production in Python async, typed, modular, and observable. You write code other engineers can reason about.

- Worked with Playwright, browser-use, or equivalent browser automation at a level beyond basic scripting.

Requirements :

- Experience with RAG systems and specifically their limits. You know why RAG alone fails for temporal reasoning, absence detection, and cross-entity traversal.

- Contributed to or built agent evaluation frameworks (RAGAS, custom evals, LLM-as-judge pipelines).

- Worked with vector stores alongside graph databases pgvector, Pinecone, and Weaviate and know when to use each. Familiarity with software testing concepts, QA workflows, or developer tooling : you don't need to be a QA engineer, but you need to understand what one worries about.

- Exposure to GitHub API, Jira API, or similar developer ecosystem integrations.

- TypeScript or Node.js exposure our frontend-adjacent agent requires it occasionally.