Software Engineer – AI (IS&T)

Apple — Bengaluru, Karnataka; Hyderabad, Telangana

2+ yrs exp · Full-time

HireSetu listing reference 642edb — confirm requirements below, then apply on the employer site.

Quick answer: Two years of applied AI/ML plus real GenAI systems, RAG pipelines, vector search and agent workflows is the bar that decides this application. The team sits in Apple's enterprise data warehouse, working on petabytes of retail, finance, sales and operations data out of Bengaluru or Hyderabad.

From Apple's job posting

Posted

Oct 15, 2026

Role Number

200689026-0321

Do you want to help build some of the largest and most consequential enterprise and customer technology systems in the world? Join Apple’s Information Systems and Technology (IS&T) organization. IS&T is the engine behind everything Apple does for customers and for the people who build for them. It’s Apple’s central nervous system. Supporting 2.5 billion active Apple devices, processing billions of secure transactions, and keeping the technology that defines modern life running flawlessly, IS&T makes the impossible feel effortless.

Do you love building solutions to handle global complexity and immense scale? Imagine what you could do here.

AI & Data Platforms (AiDP) is IS&T's engine for AI-powered innovation. The team brings together data, application development, and machine learning - including generative AI - along with data services and customer success functions, to help IS&T build solutions more efficiently and streamline the adoption and embedding of generative AI across Apple.

Description

The Enterprise Data warehouse team within AiDP deals with Petabytes of data catering to a wide variety of real- time, near real-time and batch pipelines and data centric agentic solutions on these data assets. These solutions are integral part of business functions like Retail, Sales, Operations, Finance, AppleCare, Marketing and Internet Services, enabling business drivers to make critical decisions.

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 experience, with 2+ years in software and applied AI/ML engineering

Programming proficiency in Python or Java

Experience building GenAI-powered applications, including RAG pipelines, embeddings, vector databases, and semantic search integrated with structured/unstructured enterprise data

Practical experience building AI agents or agentic workflows (planning, memory, tool use, execution loops) using frameworks such as LangChain, LlamaIndex, or similar.

Preferred Qualifications

Full-stack application development experience - able to design and ship end-to-end data/AI products spanning backend APIs, data services, and front-end/UI layers

Ability to translate ambiguous business problems into scalable technical designs, and take solutions from prototype to production with attention to governance, reliability, and observability

Strong collaboration and communication skills to work across cross-functional business and engineering teams, technical and non-technical alike

Experience building multi-agent systems/agentic workflows that autonomously coordinate retrieval, reasoning, and action steps across a data pipeline

Experience with cloud data platforms and cloud-native architectures (AWS, GCP, or Azure — AWS preferred), along with ML frameworks such as PyTorch or TensorFlow for model training and serving

Experience with modern front-end/full-stack frameworks (e.g., React, Streamlit, FastAPI) for rapidly building internal data/AI-powered applications and dashboards

Track record of brainstorming and shipping POCs using AI/ML/GenAI services to solve new or existing enterprise problems, with the ability to manage partner communications and ambiguity in a fast-changing, matrixed environment

Prior experience in enterprise domains such as Retail, Finance, Sales, Marketing, or Operations analytics is a plus

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.

Text from Apple's official job posting, formatted by HireSetu. The employer's own page is the final word on details.

The GenAI work is the filter

Python or Java and 2+ years in software plus applied AI/ML are the floor. What separates candidates is GenAI applications: RAG pipelines, embeddings, vector databases and semantic search wired into structured and unstructured enterprise data, and agents with planning, memory, tool use and execution loops built in LangChain, LlamaIndex or something similar.

A strong ML engineer with no production retrieval or agent work behind them is a stretch for this posting. The clouds, PyTorch, TensorFlow, React and FastAPI all sit in the preferred list, so a gap in one or two of those is survivable. A gap in the GenAI section is harder to argue past.

Retrieval and agent loops

Revision time is better spent on retrieval quality than on model architecture: chunking choices, embedding selection, hybrid search, and judging whether the retrieved context actually answered the question. On the agent side, work through how planning, memory, tool selection and stopping conditions fit together, and where those loops go wrong on messy data.

Have one pipeline you can walk through end to end, including what you would rebuild. The listing values people who take a solution from prototype into production, so the messy middle is the interesting part of your story.

Enterprise data underneath everything

The Enterprise Data Warehouse team runs real-time, near real-time and batch pipelines feeding Retail, Sales, Operations, Finance, AppleCare, Marketing and Internet Services. The AI sits on top of assets that already exist and already matter to the business.

Governance, reliability and observability are named alongside the modelling, which tells you the job includes keeping systems trustworthy once they are live. Anyone picturing a research post should read that part twice. The scale is petabytes, so the engineering habits that matter are the unglamorous ones: cost, latency, monitoring, and knowing what happens when a pipeline feeding your model goes stale.

Full-stack leanings and the business side

The preferred list leans full-stack: backend APIs, data services, front-end layers, and tools like React, Streamlit and FastAPI for internal dashboards and AI-powered apps over a data platform. Short proofs of concept count here, and so does translating a vague business problem into a design that holds up.

Prior exposure to retail, finance, sales, marketing or operations analytics is listed as a plus, and it genuinely helps when the stakeholders are those teams. Two locations appear, Bengaluru and Hyderabad. Confirm which office the role sits in and what the onsite expectations are before you commit to a move.

Prepared from Apple's job posting and checked against it before publishing. See our Editorial Policy.

How to apply

  1. The Apply button opens this job on Apple's careers site (jobs.apple.com).

Apply on Apple's site

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