Senior Python AI Engineer (LLM / Multi-Agent Systems)

Що робити

Agent Architecture: Design and implement complex agent orchestration logic using LangGraph. You will define state management, conditional routing, and error handling within the agent graph.
Tool Engineering: Build and optimize the tool layer (function calling) that allows LLMs to interact with internal financial APIs and databases accurately.
Performance Optimization:
Reduce end-to-end latency through asynchronous processing and streaming (SSE).
Implement semantic caching strategies to minimize API costs and response time.

Python Expert: Strong proficiency in modern Python. Deep understanding of asynchronous programming (asyncio) patterns is mandatory, as our entire I/O pipeline (Network, DB, LLM) is non-blocking. Experience with FastAPI and Pydantic (v2).
Agentic Frameworks: Production experience with LangChain. Hands-on experience or deep conceptual understanding of LangGraph (or similar state-machine-based agent frameworks).
Deep LLM Expertise (What we mean by "Deep"):
Non-determinism Management: Strategies for handling LLM hallucinations and ensuring reliable outputs (e.g., self-correction loops, specific prompting techniques like CoT/ReAct).
Structured Outputs: Experience forcing LLMs to adhere to strict schemas (Pydantic/JSON mode) for reliable downstream processing.

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