AI Data Platform Engineer - Manufacturing Systems and Infrastructure

Apple; Bengaluru; On-site; INR 4,000,000 - 7,000,000; Full time

Job summary

Apple seeks an AI Data Platform Engineer with the MSI team to design, build, and operate scalable AI data platforms enabling GenAI, Agentic AI, and Embodied AI across the enterprise. You will develop reusable platform services, data pipelines, and data quality frameworks that transform fragmented enterprise data into trusted, AI-ready datasets.

You will collaborate with AI/ML engineers, software engineers, product teams, and domain experts to define AI data requirements and deliver

Qualifications

• 5+ years designing scalable data platforms and distributed systems.
• Strong programming in Python and SQL; Java/Scala preferred.
• Experience with Airflow, Kubeflow, or MLflow for AI data pipelines.
• Experience building scalable batch and streaming data pipelines using Spark (PySpark), Kafka, Airflow, and Ray; Pandas and lakehouse tech.
• Hands-on AI data engineering including ground truth dataset creation, data curation and metadata management.
• Bachelors / Masters in Computer Science or related fields.

Responsibilities

• Design and build scalable AI data platforms, services and APIs for model development and production.
• Develop data ingestion, transformation and publishing pipelines for structured, unstructured and multimodal data.
• Build AI-ready datasets through ground truth creation, data curation, annotation workflows, dataset versioning and metadata management.
• Develop data quality frameworks, validation pipelines, observability and evaluation metrics for trusted AI datasets.
• Design and implement Retrieval-Augmented Generation (RAG) pipelines, embedding workflows and vector database integrations.
• Build scalable platform capabilities for end-to-end AI data lifecycle management including governance and secure publishing.
• Collaborate with AI/ML engineers, software engineers and product teams to define AI data requirements and deliver production-ready data solutions.
• Optimize platform scalability, reliability, performance, security and cost across cloud-native environments.
• Drive engineering best practices for AI data architecture, platform design, automation, testing, monitoring, and operational excellence.
• Evaluate emerging AI technologies and improve platform capabilities for GenAI, agentic AI, and embodied AI.

Skills

Python

SQL

Java/Scala

Airflow

Kubeflow

MLflow

PySpark

Kafka

Pandas

Delta Lake

Iceberg

Education

Bachelors / Masters in Computer Science

Tools

Kubernetes

Docker

CI/CD

Infrastructure as Code

AWS

Azure

GCP

Job description

Summary

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.

Manufacturing Systems and Infrastructure (MSI) team is an engineering organisation under the Product Operations org. MSI is responsible for the design, development and maintenance of system tools, services and applications required to efficiently run manufacturing operations at scale across global factory sites.

Description

As an AI Data Platform Engineer with the MSI team, you will design, build, and operate scalable AI data platforms that enable GenAI, Agentic AI, and Embodied AI solutions across the enterprise. You will develop reusable platform services, data pipelines, and data quality frameworks that transform fragmented enterprise and multimodal data into trusted, AI-ready datasets combining expertise in AI data platform engineering, data quality, systems engineering, and AI data lifecycle management to accelerate AI innovation.

Responsibilities

• Design, build and maintain scalable AI data platforms, services and APIs that support and enable AI model development and production.
• Develop data ingestion, transformation and publishing pipelines for structured, unstructured and multimodal data.
• Build AI-ready datasets through ground truth creation, data curation, annotation workflows, dataset versioning, and metadata management.
• Develop data quality frameworks, validation pipelines, observability and evaluation metrics to ensure trusted AI datasets.
• Design and implement Retrieval-Augmented Generation (RAG) pipelines, embedding workflows, vector database integrations and metadata services for enterprise AI applications.
• Build scalable platform capabilities for managing the end-to-end AI data lifecycle, including ground truth dataset creation, dataset versioning, metadata and lineage management, automated data quality validation, governance, and secure publishing of AI-ready datasets.
• Collaborate with AI/ML engineers, software engineers, product teams, and domain experts to define AI data requirements and deliver production-ready data solutions.
• Optimise platform scalability, reliability, performance, security, and cost across cloud-native environments.
• Drive engineering best practices for AI data architecture, platform design, automation, testing, monitoring, and operational excellence.
• Evaluate emerging AI technologies and continuously improve platform capabilities that enable GenAI, agentic AI, and embodied AI solutions.

Minimum Qualifications

• 5+ years of experience designing and building scalable data platforms and distributed systems
• Strong programming skills in Python and SQL, with proficiency in Java or Scala preferred
• Experience with Airflow, Kubeflow, or MLflow to build and orchestrate scalable AI data pipelines
• Experience building scalable batch and streaming data pipelines using Spark (PySpark), Kafka, Airflow, and Ray, with proficiency in Pandas and modern data lake/lakehouse architectures (e.g., Iceberg, Delta Lake)
• Hands-on experience with AI data engineering, including ground truth dataset creation, data curation, annotation pipelines, dataset versioning, and metadata management
• Bachelors / Masters in Computer Science or related fields

Preferred Qualifications

• Experience implementing data validation, quality frameworks, observability, and AI dataset evaluation
• Knowledge of RAG architectures, embedding generation, vector databases, and AI data preparation for LLMs and agentic AI
• Experience with cloud platforms (AWS, Azure, or GCP), Kubernetes, Docker, CI/CD, and Infrastructure as Code
• Strong understanding of distributed systems, APIs, microservices, and enterprise integration patterns
• Excellent communication, collaboration, and technical leadership skills
• Experience building platforms supporting GenAI, Agentic AI, or Embodied AI applications
• Experience with multimodal datasets, knowledge graphs, AI evaluation frameworks, or vector search technologies
• Familiarity with enterprise data governance, lineage, metadata management, and AI compliance
• Experience working with manufacturing, operational, IoT, or industrial data platforms
• Demonstrated ability to lead technical initiatives and mentor engineers

At Apple, we believe accessibility is a fundamental human right. You’ll find that idea reflected in everything here in our culture, our benefits and our digital tools. By welcoming as many perspectives as possible, we help you build a career where you feel like you belong.

Learn about accessibility in Apple’s workplace

Role Number: 200673302-0321