AI Engineer (Supply Demand Management)

We are looking for a strong AI Engineer to build the "hands and eyes" of our AI agents. While LLMs are great at reasoning, they cannot act in the real world without highly reliable, well-structured tools. In this role, you will be the foundational engineer behind our AI Tool Foundry—a centralized platform of Python-based functions, APIs, and wrappers that our intelligent agents use to fetch data, interact with external systems, and execute complex workflows. You will design how tools are standardized, how they are described to LLMs (via schemas and docstrings), and how we ensure agents execute them safely and predictably. If you love writing pristine, highly typed Python code and are fascinated by the intersection of traditional backend engineering and modern LLM function calling, this is the role for you. Minimum Qualifications Expert-Level Python: 4+ years of professional software engineering experience, with deep expertise in Python. You should be highly proficient with modern Python paradigms, including asyncio, type hinting, decorators, and advanced OOP/functional patterns.
 Pydantic & Data Validation: Extensive experience using validation libraries to strictly define data models, parse inputs, and handle serialization/deserialization.
 LLM Function Calling Experience: Hands-on experience working with the function-calling or tool-use capabilities of modern LLMs (e.g., Gemini’s tools API, or open-source equivalents).
 API & Systems Integration: Deep experience integrating with third-party APIs (REST, GraphQL, gRPC) and handling edge cases like pagination, backoffs, and chaotic external state.
 Backend Best Practices: Strong fundamentals in API design (FastAPI/Starlette), version control, CI/CD, and writing comprehensive test suites (Pytest). Preferred Qualifications Experience with agentic frameworks (e.g., LangChain, LlamaIndex, AutoGen, CrewAI) and a strong opinion on when to use them versus when to write custom orchestration. Familiarity with AST (Abstract Syntax Trees) or dynamic code execution/sandboxing (e.g., executing agent-generated Python code safely). Experience building retrieval systems (RAG) or working with Vector Databases (Pinecone, Qdrant, Weaviate).