AI Engineer - Supply Demand Management

Summary

Apple is where individual imaginations gather together, committing to the values that lead to great work. Every new product we build, service we create, or Apple Store experience we deliver is the result of us making each other’s ideas stronger. That happens because every one of us shares a belief that we can make something wonderful and share it with the world, changing lives for the better. It’s the diversity of our people and their thinking that inspires the innovation that runs through everything we do. When we bring everybody in, we can do the best work of our lives. Here, you’ll do more than join something — you’ll add something.

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 async, type hinting, decorators, and advanced OOP/functional patterns.
 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 in designing UIUX product/solutions

Description

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.

Responsibilities

Build the AI Tool Foundry: Architect and maintain a centralized repository of modular, reusable Python tools. Establish the standards, base classes, and registries that allow any internal AI agent to easily discover and consume these capabilities. Develop Agent-Facing APIs & Functions: Write robust Python functions and external API wrappers specifically designed for LLM consumption. You will obsess over developments of tools to ensure LLMs rarely hallucinate arguments. Safety, Sandboxing & Permissions: Design execution environments that allow agents to act autonomously while maintaining strict guardrails. Implement human-in-the-loop (HITL) checkpoints, rate limits, and scope-based permissions for agentic actions. Testing & Evaluation: Traditional CI/CD isn't enough for non-deterministic agents. You will build testing frameworks that not only unit-test the Python functions but also evaluate how reliably the LLM selects the correct tool and provides the correct parameters. Observability & Tracing: Implement logging and tracing (e.g., via LangSmith, Phoenix, or OpenTelemetry) to monitor agent tool usage, track latency, catch parsing errors, and debug multi-step agent reasoning loops.