Sr. Platform Software Engineer, AI & Data Platforms (AiDP)

Apple•Sunnyvale, CA

16h

About The Position

Apple’s Data Services organization provides data solutions including SQL and NoSQL databases, search engines, and streaming platforms with generative AI capabilities. The team builds and operates data platforms for large-scale data science applications and supports multiple Apple lines of business, using open-source technologies and managing large streams of data across cloud environments. The senior engineer will develop and deploy large-scale data platforms and products, including semantic and hybrid search and streaming applications. The role focuses on distributed systems, big data, AI/ML, and database-management automation. It includes designing agentic tools and frameworks, multi-agent orchestration systems, stateful conversational runtimes, automated evaluation and benchmarking, and integration of automation into CI/CD pipelines. The position also supports production operations, including incident resolution, troubleshooting, capacity planning, backup administration, and 24x7 on-call support for urgent critical issues. The engineer will improve system stability, security, efficiency, scalability, monitoring, and self-healing; document and test development and deployment processes; support development environments; and collaborate with product teams, developers, users, stakeholders, and other engineering partners.

Requirements

At least 5 years of software development and deployment experience in Kubernetes environments.
At least 3 years of experience deploying applications using Ansible, Docker, Jenkins, or related tools.
At least 5 years building robust HTTP REST APIs and backend services.
Experience building highly available software services or platforms in the big data space.
Experience designing and managing large-scale data-processing pipelines using Kafka, Spark, and streaming technologies.
Proficiency in Python; Go and Java are optional in the posting.
Bachelor’s degree in Computer Science or Computer Engineering, or equivalent work-related experience.
Validated experience in software design, testing, and source-code management.
Strong background in CI/CD practices and open-source software development.
Experience with Python and/or Go development, and programming in Go, Python, Java, or another JVM language.
Experience managing ETL pipelines, defining measurable SLAs, and handling near-real-time upstream and downstream data processing.
Expertise in Docker, Kubernetes or equivalent, Helm, and Kustomize via Flux or GitOps practices.
Proficiency with Linux/POSIX operating systems, shell scripting, and networking technologies.
Ability to address performance, scalability, reusability, and flexibility considerations in distributed databases.
Communication skills for cross-functional collaboration with program managers and engineering partners.
Comfort working in fast-paced environments while evaluating emerging technologies.
Ability to work independently, automate tasks, document work, solve problems, manage competing priorities, and resolve root causes in unfamiliar environments.

Nice To Haves

Master’s degree in Computer Science or equivalent, with 5+ years of overall industry experience designing and managing large-scale production deployments on-premises and on AWS or GCP.
Experience with public embedding models and large language models, including pre-training, fine-tuning, and optimizing data insights for accuracy and recall.
Experience designing and implementing RAG pipelines, AI agents with the CrewAI API, and autonomous workflows.
Familiarity with AI/ML tooling, LLM APIs, and integrating ML inference into production services.
Experience with infrastructure-as-code tools such as Ansible, Terraform, and Crossplane.
Experience managing and monitoring large-scale cloud services using Kubernetes probes, Splunk, Prometheus, Grafana, and Mosaic.
Mastery of logging and observability technologies for tracking system metrics.
Familiarity with ETCD, Kafka, and Spark in large-scale production systems.
Familiarity with the AI agent and MCP server stack.

Responsibilities

Develop and deploy large-scale big data semantic and hybrid search and streaming applications.
Design and build tools and frameworks based on agentic workflows for database management and performance tuning.
Integrate AI/ML capabilities such as intelligent configuration recommendations, workload-aware scheduling, and LLM-powered operator interfaces.
Design and scale production-grade multi-agent orchestration systems and stateful conversational runtimes.
Develop automated evaluation harnesses for continuous offline and online ground-truth benchmarking and regression testing.
Integrate database-management automation into CI/CD pipelines with automated testing, canary rollouts, and rollback capabilities.
Troubleshoot, mitigate, and resolve production issues involving applications, databases, networks, and proxies using monitoring and debugging tools.
Develop and enhance monitoring, alerting, and health-check systems that provide visibility into database health.
Build self-healing mechanisms to detect and remediate known failure patterns automatically.
Provide incident resolution for technical production issues and 24x7 on-call support for urgent critical issues.
Document configuration, procedures, status, and development and deployment processes; provide justifications and train users on complex topics.
Guide improvements to system stability, security, efficiency, and scalability; determine future capacity needs and investigate new products and features.
Administer and ensure proper execution of backup systems.
Support and advance development environments to reduce friction when shipping to production.
Plan and develop features in collaboration with product and development teams, users, and stakeholders.
Review code and collaborate on pull requests with team members.
Identify and optimize API endpoint performance and MCP server bottlenecks affecting user workflows.
Learn and improve technical skills through code and documentation review, experiments, and collaboration.