Ai/machine Learning Engineer

Apple · Big Tech · Bangalore, India · Software and Services

AI/ML Engineer to build intelligent systems and deploy state-of-the-art AI models and systems across Apple's business groups. Responsibilities include implementing ML infrastructure, developing feature engineering and fine-tuning frameworks, designing ML pipelines, and optimizing models. The role requires designing systems from raw data to autonomous action, implementing RAG pipelines, working with embeddings and vector databases, building AI agents with tool use, and fine-tuning transformer models. Experience with large-scale data and ML frameworks is essential.

What you'd actually do

work on building intelligent systems to democratize AI across a wide range of solutions within Apple.
drive the development and deployment of state-of-the-art AI models and systems that directly impact the capabilities and performance of Apple’s products and services.
implement robust, scalable ML infrastructure, including data storage, processing, and model serving components, to support seamless integration of AI/ML models into production environments.
develop novel feature engineering, data augmentation, prompt engineering and fine-tuning frameworks that achieve optimal performance on specific tasks and domains.
design and implement automated ML pipelines for data preprocessing, feature engineering, model training, hyper-parameter tuning, and model evaluation, enabling rapid experimentation and iteration.

Skills

Required

Bachelor’s Degree or Equivalent with 4+ years of experience in AI/ML & Data engineering
Ability to design systems that go from raw data → structured insight → autonomous action, closing the loop between data engineering and AI decision-making
Design and implement RAG (Retrieval-Augmented Generation) pipelines that feed structured data, user personas, and domain-specific context into LLMs for accurate, grounded outputs
Deep understanding of embeddings, vector databases, and semantic search to power retrieval layers in AI applications
Hands-on experience building AI agents with memory, planning, tool use, and execution loops using frameworks such as LangChain, LlamaIndex.
Proven expertise with transformer-based models (BERT, GPT, LLaMA, etc.) including fine-tuning, prompt engineering, and understanding of their underlying attention mechanism
Proficiency with ML frameworks such as PyTorch or TensorFlow, including model training, optimization, and serving pipelines
Experience working with data at scale (peta bytes) with big data tech stack and advanced programming languages e:g Python, Scala.
Database development experience with Relational or MPP/distributed systems such as Snowflake, SingleStore along with expertise in SQL and Advance SQL.
Experience in designing and building dimensional data models to improve accessibility, efficiency and quality of data

Nice to have

Experience building multi-agent AI systems and agentic workflows that coordinate across data retrieval, reasoning, and action steps autonomously is a plus
Experience in modern cloud warehouse, data lakes and implementation experience on any of the cloud platforms like AWS/GCP/Azure - preferably AWS.
Have continuous focus to Brainstorm and Design various POCs using AI/ML Services for new or existing enterprise problems

What the JD emphasized

Ability to design systems that go from raw data → structured insight → autonomous action, closing the loop between data engineering and AI decision-making
Design and implement RAG (Retrieval-Augmented Generation) pipelines that feed structured data, user personas, and domain-specific data
Hands-on experience building AI agents with memory, planning, tool use, and execution loops
Experience building multi-agent AI systems and agentic workflows that coordinate across data retrieval, reasoning, and action steps autonomously is a plus

Other signals

building intelligent systems
democratize AI
state-of-the-art AI models and systems
implement robust, scalable ML infrastructure
develop novel feature engineering, data augmentation, prompt engineering and fine-tuning frameworks
design and implement automated ML pipelines
implement advanced model compression and optimization techniques
design systems that go from raw data → structured insight → autonomous action
Design and implement RAG pipelines
Deep understanding of embeddings, vector databases, and semantic search
Hands-on experience building AI agents with memory, planning, tool use, and execution loops
Proven expertise with transformer-based models
Proficiency with ML frameworks such as PyTorch or TensorFlow
Experience working with data at scale
Experience building multi-agent AI systems and agentic workflows

Read full job description

The people here at Apple don’t just build products — we craft the kind of wonder that’s revolutionized entire industries. It’s the diversity of those people and their ideas that supports the innovation that runs through everything we do, from amazing technology to industry-leading environmental efforts. Join Apple, and help us leave the world better than we found it.

The Artifical Intelligence and Data platform team provides data services, analytics, reporting, and data science solutions to Apple’s business groups, including Retail, iTunes, Marketing, AppleCare, Operations, Finance, and Sales. These solutions are built on top of an end-to-end machine learning platform with sophisticated AI capabilities. This position is an extraordinary opportunity for a competent, experienced, and results-oriented machine learning engineer to define and build some of the best-in-class machine learning solutions and tools for Apple.

Description

As a Artifical intelliegnce / Machine Learning Engineer, you will work on building intelligent systems to democratize AI across a wide range of solutions within Apple. You will drive the development and deployment of state-of-the-art AI models and systems that directly impact the capabilities and performance of Apple’s products and services. You will implement robust, scalable ML infrastructure, including data storage, processing, and model serving components, to support seamless integration of AI/ML models into production environments. You will develop novel feature engineering, data augmentation, prompt engineering and fine-tuning frameworks that achieve optimal performance on specific tasks and domains. You will design and implement automated ML pipelines for data preprocessing, feature engineering, model training, hyper-parameter tuning, and model evaluation, enabling rapid experimentation and iteration. You will also implement advanced model compression and optimization techniques to reduce the resource footprint of language models while preserving their performance. .You should be able to (i) understand a business challenge, (ii) Collaborate with business and other cross functional teams (ii) design a statistical or deep learning solution to find the needed answer to it, (iii) developing it by yourself or guide another person to do it, (iv) deliver the outcome into production, (v) Keep a good governance of your work. There are massive opportunities for you deliver impactful influences to Apple.

Minimum Qualifications

Bachelor’s Degree or Equivalent with 4+ years of experience in AI/ML & Data engineering Ability to design systems that go from raw data → structured insight → autonomous action, closing the loop between data engineering and AI decision-making Design and implement RAG (Retrieval-Augmented Generation) pipelines that feed structured data, user personas, and domain-specific context into LLMs for accurate, grounded outputs Deep understanding of embeddings, vector databases, and semantic search to power retrieval layers in AI applications Hands-on experience building AI agents with memory, planning, tool use, and execution loops using frameworks such as LangChain, LlamaIndex. Proven expertise with transformer-based models (BERT, GPT, LLaMA, etc.) including fine-tuning, prompt engineering, and understanding of their underlying attention mechanism Proficiency with ML frameworks such as PyTorch or TensorFlow, including model training, optimization, and serving pipelines Experience working with data at scale (peta bytes) with big data tech stack and advanced programming languages e:g Python, Scala. Database development experience with Relational or MPP/distributed systems such as Snowflake, SingleStore along with expertise in SQL and Advance SQL. Experience in designing and building dimensional data models to improve accessibility, efficiency and quality of data

Preferred Qualifications

Experience building multi-agent AI systems and agentic workflows that coordinate across data retrieval, reasoning, and action steps autonomously is a plus Experience in modern cloud warehouse, data lakes and implementation experience on any of the cloud platforms like AWS/GCP/Azure - preferably AWS. Have continuous focus to Brainstorm and Design various POCs using AI/ML Services for new or existing enterprise problems

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