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# AI/ML Engineer, Applied Data Science

- **Company:** Apple Inc.
- **Location:** Cupertino, CA, United States
- **Experience:** Experienced
- **Salary:** $172,100.0 - $258,600.0
- **Contract:** Permanent contract
- **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Graph Database, Python (Programming Language), Machine Learning, Software Product Management, Rapid Prototyping Process, Tensorflow, Pytorch, Retrieval-Augmented Generation, Large Language Models, Prompt Engineering, Kubernetes, Virtual Agents
- **Published:** May 21, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=3c65780128c07987

## About the Role

Do you have experience in Rapid prototyping?, 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

## Description

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.","responsibilities":"Prototype AI solutions to rapidly validate use cases and demonstrate feasibility

Scale successful prototypes into production-grade systems with reliability, monitoring, and maintainability

Implement prompt engineering solutions optimized for legal use cases

Build and optimize RAG (Retrieval-Augmented Generation) pipelines using vector databases and knowledge graphs

Develop context engineering approaches that leverage the semantic layer for improved accuracy

Implement grounding mechanisms to reduce hallucinations and improve factual accuracy

Build evaluation frameworks to measure AI accuracy, relevance, and safety across use cases

Integrate AI capabilities with legal workflows (CLM, matter management, eBilling)

Develop AI agent solutions for automation use cases across legal operations

Implement guardrails and safety mechanisms for production AI systems

Collaborate with AI Architect on system design and technical standards

Support AI Product Developers with APIs, integration patterns, and technical guidance


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