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JD #2390
Applications open Full-Time Job

Software Engineer : AI

Apple

  • Bengaluru, India
  • β‚Ή20 LPA - β‚Ή30 LPA (approx.)
  • Fresher (0-1 Yrs)
  • Posted Today
HR
Recruiter contact
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About the role

Apple Inc. is a global technology leader known for its innovative hardware, software, and services that touch the lives of billions of people. With a legacy of design excellence, privacy‑first philosophy, and a relentless focus on user experience, Apple has built an ecosystem that spans iPhone, iPad, Mac, Apple Watch, and a suite of cloud‑based services. The company’s Information Systems & Technology (IS&T) division acts as the central nervous system, ensuring that every Apple device and service runs smoothly, securely, and at massive scale. In India, Apple’s engineering hubs in Bengaluru are expanding rapidly, offering fresh talent the chance to work on world‑class products while collaborating with cross‑functional teams across the globe. The role of Software Engineer – AI sits within the AI & Data Platforms (AiDP) team, a high‑impact group that powers AI‑driven innovation across Apple’s enterprise landscape. The team owns petabyte‑scale data warehouses, real‑time pipelines, and generative‑AI services that enable business units such as Retail, Finance, Operations, and Marketing to make data‑informed decisions. As a fresher, you will be mentored to translate complex business problems into scalable AI solutions, build and ship generative‑AI applications, and ensure robust governance and observability of production systems. **Key Responsibilities** 1. Understand and dissect business challenges from Retail, Finance, or Operations domains. 2. Design statistical or deep‑learning models, including generative‑AI and retrieval‑augmented generation (RAG) pipelines. 3. Develop end‑to‑end solutions using Python/Java, vector databases, and semantic search. 4. Build AI agents or multi‑agent workflows with frameworks like LangChain or LlamaIndex. 5. Deploy models and services on cloud platforms (AWS preferred) with CI/CD pipelines. 6. Create backend APIs and lightweight front‑end dashboards (React, Streamlit, FastAPI). 7. Implement monitoring, logging, and governance to ensure reliability and compliance. 8. Collaborate with cross‑functional stakeholders, presenting insights and technical roadmaps. 9. Contribute to proof‑of‑concepts that showcase the value of GenAI across enterprise use‑cases. 10. Mentor peers and document best practices for AI development within the team. **Tech Stack**: Python, Java, PyTorch/TensorFlow, LangChain, LlamaIndex, AWS (S3, SageMaker, Lambda), Vector DBs (Pinecone, Milvus), React/Streamlit, FastAPI, Docker, Kubernetes. **Growth Path**: Starting as an AI Software Engineer, you can progress to Senior Engineer, Lead AI Engineer, and eventually AI Product Manager or Architecture Lead, with exposure to global product teams and opportunities to influence Apple’s AI strategy. **Why Join Apple?** Apple offers a culture of relentless innovation, world‑class mentorship, and the chance to work on products that impact billions. Fresh graduates gain access to cutting‑edge AI research, a collaborative environment, and generous employee benefits that prioritize work‑life balance and personal growth.

Eligibility & process

Who can apply

Bachelor's degree in Computer Science, Computer Engineering, Information Technology, Electronics & Communication, or related fields; minimum 60% aggregate (or CGPA 6.5/10); graduating batch 2024‑2026; no active backlogs at the time of joining; strong academic record in algorithms, data structures, and machine learning.

Skills & stack

Python Java Machine Learning Deep Learning Generative AI Retrieval‑Augmented Generation Langchain Llamaindex

Selection process

  1. Round 1: Online Coding Assessment (DSA & AI fundamentals)
  2. Round 2: Technical Interview (system design, AI/ML concepts, coding)
  3. Round 3: Managerial/HR Interview (fit, culture, motivation)

Prep kit

19 resources
Study material 4
Company-specific guides 4
Likely coding questions 4
Round-wise strategy 4
Round 1: Online Assessment (OA) Aptitude, Quantitative Logic & 2 Coding Problems Focus on accuracy and speed. Practice arrays, strings, and standard arithmetic puzzles.
Round 2: Technical Interview 1 Core Tech Stack (Python, Java, Machine Learning, Deep Learning, Generative AI, Retrieval‑Augmented Generation, Langchain, Llamaindex) & Live Code Tracing Explain your thought process aloud. Analyze time & space complexities before coding.
Round 3: System Design & Problem Solving Database Schemas, APIs & Architecture Basics Clarify edge cases, diagram schemas cleanly, and discuss scalability trade-offs.
Round 4: HR & Cultural Fit Discussion Apple Core Values, Learning Agility & Offer Terms Demonstrate passion, strong communication, and readiness for full-time collaboration.
Technical questions 4
What is the bias-variance tradeoff and how do you prevent overfitting? Answer
Model answer: High bias leads to underfitting (oversimplified model), high variance leads to overfitting (captures noise). Mitigate using L1/L2 Regularization, Dropout, Cross-Validation, and data augmentation.
Explain the difference between Precision, Recall, and F1-Score. Answer
Model answer: Precision = TP / (TP + FP) (correctness of positive predictions). Recall = TP / (TP + FN) (coverage of actual positives). F1-Score is the harmonic mean of Precision and Recall.
How does Gradient Descent work and what is the role of Learning Rate? Answer
Model answer: It optimizes loss functions by iteratively moving weights in the direction of negative gradient. A large learning rate may overshoot the minimum; a small rate causes slow convergence.
What is the difference between Supervised, Unsupervised, and Self-Supervised learning? Answer
Model answer: Supervised uses labeled data (X -> y). Unsupervised finds hidden patterns in unlabeled data (clustering/PCA). Self-supervised generates labels from input data (e.g. masked language modeling in BERT/Transformers).
Behavioural & HR questions 3
Why do you want to join Apple as a Software Engineer : AI?
Highlight Apple's market reputation, recent tech innovations, and how your skills in Python directly solve their team's objectives.
Describe a challenging bug or academic project roadblock and how you resolved it.
Use the STAR method: Situation (project context), Task (what needed solving), Action (specific tools/logic applied), Result (quantifiable positive outcome).
How do you handle strict deadlines or sudden scope changes?
Explain your prioritization strategy, proactive communication with mentors/peers, and agile mindset.
Apple β‚Ή20 LPA - β‚Ή30 LPA (approx.)
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