Site Reliability Engineer (Data Platform / ML Infrastructure)

Quick Facts

• Role: Site Reliability Engineer (Data Platform / ML Infrastructure)

• Focus: Production operations and reliability for multi-cloud data and ML/AI platform services

Description

As an SRE on Apple Data Platform, you will operate and support the platform’s full portfolio, from big data pipelines to multi-cloud infrastructure, and grow into a go-to expert for ML/AI platform services. You will keep services, pipelines, and platforms running reliably in production (without building models yourself), including Ray training/serving, LangGraph agent deployments, RAG architectures, embeddings platforms, and vector store platforms.

Responsibilities

• Operate, monitor, and triage production and non-production environments across the ADP portfolio (data processing, ML/AI, and multi-cloud infrastructure)

• Participate in a rotating on-call schedule, including occasional weekday and weekend coverage

• Own the operational health of ML/AI platform services as SME (Ray, LangGraph agents, RAG pipelines, embeddings, and vector stores)

• Provide Slack-based support to internal customers; screen, triage, and resolve service-related issues

• Partner with dev teams across time zones to onboard new services; design monitoring, alerting, and dashboards using Prometheus, Grafana, and Splunk

• Build automation and self-healing tooling to reduce manual toil and scale operational capacity

• Identify, escalate, and resolve production issues to protect platform reliability and customer experience

• Collaborate with SRE and developer partner teams, engineering, and program management to align execution with team and org goals

Requirements

• Bachelor’s Degree in Computer Science, an engineering-related field, or equivalent related experience

• 3+ years in Site Reliability Engineering, DevOps, or Infrastructure-focused role

• Proficient in Python; working knowledge of Golang is a plus

• Experience with Kubernetes and at least one major cloud provider (AWS or GCP)

• Exposure to operating or supporting ML pipelines, model-serving infrastructure, or LLM-based systems in production

• Strong communication skills and composure under pressure during incidents

• Solid grounding in SRE principles, with prior on-call or production-support experience

Benefits

• Opportunity to build deep expertise across a technically diverse, multi-cloud data and ML/AI platform

• Ownership of reliability roadmap for ML/AI platform services and internal customer support

• Room to grow scope and impact as the team’s go-to expert for ML/AI infrastructure evolution