22 hours ago
Responsibilities
- Design, build, and operate scalable AI data platforms, services, APIs, and architectures for agentic AI workflows.
- Develop ingestion, transformation, publishing, batch, and streaming pipelines for structured, unstructured, and multimodal data.
- Implement RAG pipelines, embedding workflows, vector database integrations, and metadata services for enterprise AI applications.
- Build tools that make complex AI systems observable, understandable, and debuggable.
- Optimize scalability, reliability, performance, security, and cost across cloud-native and agentic systems.
- Collaborate with AI/ML engineers, software engineers, product teams, and domain experts to deliver production-ready data solutions.
- Prototype and iterate on hands-on AI products and solutions.
- Translate complex user workflows and technical problems into practical solutions and communicate business-focused insights.
- Lead technical initiatives and mentor engineers.
Requirements
- Bachelor’s or master’s degree in computer science, software engineering, data engineering, or a related field.
- 8+ years of experience designing and building scalable data platforms and distributed systems.
- Strong programming skills in Python and SQL, with Java or Scala proficiency preferred.
- Experience with Airflow, Kubeflow, or MLflow for scalable AI data pipeline orchestration.
- Experience building scalable batch and streaming pipelines using Spark, PySpark, Kafka, Airflow, and Ray, with Pandas and modern data lake or lakehouse architectures such as Iceberg and Delta Lake.
- Knowledge of RAG architectures, embedding generation, vector databases, and AI data preparation for LLMs and agentic AI.
- Experience with AWS, Azure, or GCP, Kubernetes, Docker, and infrastructure as code.
- Experience with Generative AI, LLMs, agentic systems, RAG workflows, and integrating LLMs into existing systems is preferred.
- Experience with API design and manufacturing, operational, IoT, or industrial data platforms is preferred.
- Ability to collaborate across teams, communicate complex technical concepts clearly, and apply sound engineering judgment when reviewing AI-assisted code.
- Demonstrated ability to lead technical initiatives and mentor engineers.
Tech Stack
Apache AirflowApache KafkaApache SparkAWSAzureDockerGoogle Cloud PlatformJavaKubernetesMLflowPandasPythonScalaSQL
Categories
AI ApplicationsData Engineering
About Apple
Apple designs and sells consumer electronics, software, and services for consumers and professionals worldwide, including iPhone, Mac, iPad, Apple Watch, and AirPods, plus platforms like iOS/macOS and services such as the App Store, iCloud, Music, and TV+. Its business combines device sales with services and subscriptions and in-house silicon design. Founded in 1976, Apple is headquartered in Cupertino, California, and trades on NASDAQ as AAPL.