Job Description
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.
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.
Preferred Qualifications
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
Minimum Qualifications
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
