Posted on: 17/04/2026
Description :
- Design automation workflows that coordinate multiple AI agents across business functions.
- Implement evaluation frameworks and test harnesses for LLM-based systems.
- Build reliable, deterministic pipelines on top of probabilistic AI outputs.
- Create the primitives and abstractions that other teams use to deploy agents.
Requirements :
- Strong Python fundamentals; TypeScript experience valued.
- System design for distributed, async workloads and API design and integration patterns.
- Database design for AI/ML workloads (vector stores, event sourcing) and LLM engineering.
- Deep understanding of LLM primitives : prompts, context windows, tool use, structured outputs, and embeddings.
- Experience with agentic frameworks (LangChain, LangGraph, CrewAI, AutoGen, or similar).
- Familiarity with AI-assisted development tools (Claude Code, Cursor, Copilot, Aider).
- Understanding of RAG architectures and retrieval patterns.
Reliability and Testing for AI Systems :
- Experience building evaluation frameworks for LLM outputs (accuracy, latency, cost).
- Techniques for making AI workflows reproducible and debuggable.
- Understanding of guardrails, validation, and human-in-the-loop patterns.
Preferred Experience :
- Implemented CI/CD pipelines that include LLM evaluation steps.
- Experience with MCP (Model Context Protocol) or similar tool-use standards.
- Contributed to or used agent frameworks for workflow automation.
- Familiarity with cost optimization for LLM-heavy workloads
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