Agentic AI Engineer

As an Agentic AI Engineer, you will sit at the intersection of applied AI research and production engineering. You will architect agent systems that use LLMs as reasoning engines, integrate external tools and APIs, and operate reliably in dynamic, open-ended environments.

Key Responsibilities

• Architect multi-agent systems using frameworks such as LangGraph, AutoGen, CrewAI, or custom orchestration layers, with a focus on reliability and observability.
• Design and implement tool-use pipelines — including function calling, MCP integrations, browser agents, and code interpreters — that enable models to interact with real-world systems.
• Build memory systems (short-term, episodic, and semantic) and context management strategies to support long-horizon task completion.
• Define and run evaluation frameworks for agentic performance: task completion rates, error propagation, cost-efficiency, and latency benchmarks.
• Collaborate with product, research, and data teams to translate business goals into concrete agent specifications and acceptance criteria.
• Establish guardrails, safety checks, and human-in-the-loop escalation paths to ensure agents behave predictably in edge cases.
• Monitor deployed agents in production using tracing tools (LangSmith, Weights & Biases, custom observability stacks) and iterate based on failure analysis.
• Stay current with the rapidly evolving agentic AI landscape and advocate for adoption of relevant advances internally.

Core Required

Python (advanced)

LLM APIs

Agent frameworks

RAG & vector stores

Tool / function calling

Prompt engineering

REST & async APIs

Docker / cloud

Evaluation design

Git & CI/CD