<p>Imagine what you could do here. At Apple, new ideas have a way of becoming great products, services, and customer experiences very quickly. Bring passion and dedication to your job and there's no telling what you could accomplish. Are you passionate about taking AI from prototype to production at scale? Do you enjoy the craft of prompt engineering, retrieval optimization, and grounding? Can you build AI systems that are not just impressive demos, but reliable production tools? The Applied Data Science team within Legal Operations is building production-grade AI for a global legal organization. The AI/ML Engineer role is central to this mission - prototyping AI solutions, then scaling them to production systems that attorneys rely on every day.</p> <p>The AI/ML Engineer builds AI capabilities from prototype to production. You will develop prompt engineering solutions, RAG pipelines, AI agents, and evaluation frameworks - starting with rapid prototypes to validate use cases, then engineering them into scalable, production-grade systems. This role requires both the creativity to explore what's possible and the rigor to build what's reliable.</p> <p>Experience with LangChain, LlamaIndex, or similar LLM orchestration frameworks Experience with agentic AI frameworks (LangGraph, CrewAI, or similar) Familiarity with knowledge graphs and GraphRAG patterns Experience with AI evaluation tools (RAGAS, DeepEval, or similar) Knowledge of legal domain and legal NLP applications Experience with guardrails and safety frameworks (Guardrails AI, NeMo Guardrails) Understanding of MCP (Model Context Protocol) or similar integration patterns Experience deploying and monitoring AI systems at scale Track record of shipping AI products that users rely on</p> <p>4+ years of delivering solutions in AI/ML engineering, NLP, or related roles Strong proficiency in Python and ML frameworks (PyTorch, TensorFlow, or similar) Experience with LLM APIs (OpenAI, Anthropic, or similar) Experience with RAG architectures and vector databases Understanding of prompt engineering techniques and best practices Experience taking AI systems from prototype to production Experience with evaluation frameworks for AI systems Supporting AI applications in production</p>