Description

Key Skills: Agentic AI, Retrieval-Augmented Generation (RAG), Lang graph

Roles and Responsibilities:

• Architect and implement multi-agent orchestration patterns using LangGraph, including skills-based workflows across the agent chain.
• Design and evolve prompting architectures and RAG strategies to deliver high-fidelity, reliable outputs grounded in knowledge bases.
• Integrate Model Context Protocol (MCP) to improve context grounding and ensure robust AI service behavior in production.
• Conduct technical design reviews and define engineering best practices for AI service development, including compliance with EU AI Act, DORA, and internal DevSecOps standards.
• Evaluate and benchmark foundation models (Claude, GPT, Gemini) for agent tasks, optimizing accuracy, latency, and cost across the agent pipeline.

Skills Required:

• 8 - 13 years of experience building AI/ML systems; at least 2 years delivering LLM-based applications in production.
• Agentic AI and multi-agent workflow design for LLM-based applications.
• Retrieval-Augmented Generation (RAG) architectures and prompt/RAG strategy design.
• Production-grade reliability practices for AI outputs (evaluation, benchmarking, and quality controls).
• AI service engineering with grounded knowledge retrieval and agent pipeline integration.

Good to Have:

• LangGraph experience for multi-agent orchestration.

Education: Bachelor's or Master's degree in Computer Science, AI, or a related field

Key Skills

Education

Any Graduate