Sr. Agentic Service Engineer

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.

Apple Pay brought mobile payment to millions of customers, and it's just the beginning. We are looking for engineers who enjoy both hands-on technical work and designing thoughtful, scalable services for Wallet and Apple Pay. Our team's vision is to be the engine of intelligent transformation, leveraging a unified, reliable data platform to build and deploy innovative and solutions that drive significant business impact and enable data-driven decision-making throughout the organization.

Description

As a Senior Software Engineer for AI Agents, you will define the technical vision and own the architectural evolution of platform infrastructure that enables AI agents to thrive at Apple scale. You will drive strategic platform decisions, mentor engineering teams, and influence technical direction while building and optimizing the platform that supports our agentic ecosystem - from data pipelines and compute orchestration to monitoring and reliability systems. ","responsibilities":"You'll play a pivotal role in defining modular agent architectures integrating powerful foundation models (LLMs and beyond)

Architect platform solutions that deliver flexibility, resilience, and cost optimization while meeting stringent security and privacy requirements.

Collaborating with AI/ML teams, data scientists, and infrastructure organizations, you'll translate the unique requirements of agentic workloads into scalable platform capabilities that enable rapid innovation.

Lead multi-functional technical initiatives, establish platform engineering standards, and drive the strategic roadmap for AI agent infrastructure.

Drive the real-time orchestration of agents that interact with data, APIs, and users-unlocking smarter, faster, and more adaptive applications at scale.

Preferred Qualifications

Bachelor's or Master's Degree Computer Science, Artificial Intelligence, Machine Learning, or a related field, or related experience.

Communicating effectively, both written and verbal, with technical and non-technical multi-functional teams.

Proven track record of building enterprise-grade ML pipelines (data prep, distributed training, optimization, monitoring) in cloud environments (AWS, GCP, Azure).

Extensive knowledge with popular LLMs such as Gemini, Claude, and GPT

Demonstrated experience with building AI agents, LLMs for tool use, and Multimodal-LLMs.

Experience using one or more of the following: Reinforcement learning, Distillation, RLHF fine-tuning, RAG/VectorDB, LLM uncertainty quantification.

Experience with ML frameworks (e.g., PyTorch, TensorFlow, scikit-learn)

Understanding of AI memory systems, including contextual embeddings and long-term memory management; bonus for experience with LangMem or LangGraph

Experience working on platform engineering or a developer experience platform is a plus.

Hands-on experience with cloud services and containerization technologies

Minimum Qualifications

5+ years in ML engineering with experience in large-scale software system design and implementation.

Strong understanding of generative AI models, particularly large language models (LLMs) with proven experience in building complex agentic systems using LLMs.

Strong Python programming skills, with a background in developing scalable and robust services using FastAPI or similar frameworks.

Experience in Machine Learning with a particular emphasis on Large Language Models (LLMs), Retrieval Augmented Generation(RAG) and Generative AI.","internalDetails":null,"eeoContent":null