Site Reliability Engineer, Apple Data Platform - Jobs - Careers at Apple
- * 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 across supported services, including occasional weekday and weekend coverage.
- * Own the operational health of ML/AI platform services as SME — driving reliability, support, and customer guidance for Ray, LangGraph agents, RAG pipelines, embeddings, and vector store platforms.
- * 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 — understanding architecture, then designing monitoring, alerting, and dashboards (Prometheus, Grafana, Splunk).
- * Build automation and self-healing tooling that reduces manual toil and scales the team's operational capacity.
- * Identify, escalate, and resolve production issues to protect platform reliability and customer experience.
- * Collaborate with SRE and dev partner teams, engineering, and program management to align execution with team and org goals.
- * Bachelor's Degree in Computer Science, an engineering-related field, or equivalent related experience.
- * 3+ years in a Site Reliability Engineering, DevOps, or Infrastructure-focused role.
- * Proficient in Python; working knowledge of Golang 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.
- * Hands-on experience operating or supporting Ray (training/serving), LangGraph or similar agent orchestration frameworks, RAG architectures, embeddings platforms, or vector store platforms.
- * Familiarity with MCP-based tooling and ML lifecycle/dataset management systems.
- * Experience with S3 and cloud storage/networking fundamentals.
- * Familiarity with observability tooling: Prometheus, Grafana, Splunk, PagerDuty.
- * Working knowledge of CI/CD pipelines and deployment workflows.
- * Deep understanding of one or more Big Data technologies (Spark, Flink, Airflow, Trino, Notebooks).
- * A track record of automating manual operations through scripting or tooling.
- * Intellectual curiosity and a drive to keep learning — for yourself, your team, and the org.
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Required Skills
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🇬🇧 English