Lead Agentic AI Engineer

Agentic AI Engineer

Summary Design and build autonomous AI agents, multi-agent systems, and the orchestration layers that make them reliable in production. You'll turn LLM capabilities into agents that plan, use tools, and complete real tasks end to end.

Role Expectations

• Architect and build single- and multi-agent systems using frameworks like LangChain/LangGraph, CrewAI, and AutoGen.
• Design orchestration layers: task planning, agent routing, memory, state management, and error/recovery handling.
• Implement robust tool-use and function-calling - connecting agents to APIs, databases, and internal services.
• Build and optimize RAG pipelines (retrieval, chunking, re-ranking, grounding) to reduce hallucination and improve task accuracy.
• Own agent evaluation and iteration: define success metrics, run experiments, and harden agents against edge cases.
• Collaborate with product and platform teams to ship agents that meet latency, cost, and reliability targets.

Required Skills

• 7+ years building software, with hands-on building LLM-powered agents or applications.
• Strong Python; solid grasp of prompt engineering and LLM behavior.
• Hands-on experience with at least one agent framework (LangGraph, CrewAI, AutoGen, or equivalent).
• Practical experience with tool-use/function-calling and RAG pipelines and vector databases.
• Ability to debug non-deterministic systems and design for failure and reliability.

Value adds:

• Open-source contributions to agent or LLM tooling.
• Experience with multi-agent orchestration at scale (concurrency, cost control, observability).
• Fine-tuning or model-adaptation experience.

Core Stack:

LangChain/LangGraph · CrewAI · AutoGen · Tool-Use & Function Calling · RAG · Multi-Agent Orchestration · Python