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Interview prep that actually helps you get the job
Deep-dive guides on system design, DSA, and cracking top Indian product company interviews — written for engineers, by engineers.
Featured Guide
The Complete System Design Interview Guide for Indian Product Companies
Everything you need to crack the HLD round at Swiggy, Razorpay, PhonePe, CRED, Meesho, and Flipkart — from the 45-minute framework to numbers every SDE must memorise.
All Guides
How to Crack Swiggy & Zomato SDE Interviews (2026 Guide)
A no-fluff breakdown of the Swiggy and Zomato engineering interview process — round structure, DSA patterns they favour, system design topics they probe, and what differentiates candidates who get offers.
The 45-Minute HLD Framework: How to Ace Any System Design Interview
A battle-tested, minute-by-minute framework for structuring your system design answer — from requirements gathering to trade-off discussion — used by engineers who've cracked FAANG and unicorn interviews.
System Design Interview Cheat Sheet — Save This Page
The one reference page you need before walking into any system design interview. Capacity estimation formulas, database selection guide, caching patterns, CAP theorem, and 30+ design decisions distilled to their essentials.
SQL Interview Questions for Data Engineering — Joins, GROUP BY, Window Functions, CTEs
The four SQL primitives that show up in every data engineering interview: LEFT JOIN anti-joins for orphan detection, GROUP BY with HAVING for duplicates, ROW_NUMBER vs RANK vs DENSE_RANK for top-N ranking, and CTE composition with recursive CTEs — with worked examples and full traces.
Agentic AI Engineer Interview Preparation 2026 — The Complete Guide
The role didn't exist 2 years ago. Now every AI lab and enterprise team is hiring for it. This guide covers what agentic AI engineers actually build, how interviews are structured, 10 real questions with model answers, and a 3-month prep plan.
MLOps Interview Questions and Roadmap — What's Actually Asked in 2026
MLOps interviews have matured. Companies now test CI/CD for ML, feature stores, model serving, and production drift detection — not just pandas and scikit-learn. This guide covers the real question bank, a domain-by-domain breakdown, and a 4-month roadmap.
How to Prepare for an AI Infrastructure Engineer Interview
AI infra is the most technically demanding role in the AI hiring market right now. You need GPU architecture, distributed training, and inference optimization — not just software engineering. This guide breaks down exactly what's tested and how to prepare.
RAG System Design Interview Questions — How to Design a Retrieval-Augmented Generation System
RAG system design is now a standard interview question at any company building AI products. This guide walks through chunking, vector databases, hybrid retrieval, reranking, and evaluation — with a complete model answer for the design question.
LLM Engineer Interview Preparation Plan — From Transformers to Production
LLM engineering interviews test transformer internals, fine-tuning (LoRA, QLoRA), RLHF, inference serving, and evaluation — not just API calls. This is the preparation plan that gets you from zero to interview-ready in 4 months.
AI Engineering Interview Guide 2026 — What's Actually Being Asked
The bar has shifted dramatically since 2024. Companies now test transformer internals, inference serving, agent reliability, and evaluation — not just API usage. Here's what every AI engineering interview actually tests in 2026.
System Design Interview Questions 2026 — New AI-Era Patterns
System design interviews now include AI components as standard. Classic questions evolved — design a newsfeed now means a personalized ranked feed with ML. Five new AI-era questions didn't exist in prep guides 3 years ago. Here's the full picture.
Agentic AI in 2026 — What's Working, What's Broken, and Where Careers Are Heading
Not hype, not doom. An honest technical assessment of where agentic AI actually is: what works in production, where the field is genuinely stuck, which companies are winning, and what skills will matter in 2027–2028.
Behavioral Interview Questions for Engineers — Why STAR Isn't Enough Anymore
Behavioral rounds have a higher fail rate than coding rounds at Amazon. Not because engineers can't tell stories — but because they don't understand what's being measured. This guide covers what interviewers actually look for, the DSTAR structure that works, and the 10 questions asked at 80% of interviews.
Object-Oriented Design Interview Questions 2026 — Machine Coding and LLD
LLD rounds trip engineers who crack hard LeetCode problems. The parking lot, food delivery, and ride-sharing machine coding questions test a completely different skill: translating real-world systems into clean, extensible OOP code under pressure. Here's the 45-minute framework and the 5 problems that appear most.
Distributed Systems Interview Questions 2026 — What Senior Engineers Get Asked
Distributed systems is still the highest-signal technical domain for senior interviews. In the AI era it matters more — training clusters, model serving, and vector database replication are all distributed systems problems. This covers consistency models, Raft, failure patterns, and 10 questions with full answers.
SQL Interview Questions 2026 — The Most Underrated Skill in AI Engineering
SQL is more important in the AI era than it was before it — feature stores, ML pipelines, LLM output analysis, and data quality validation all run on SQL. This covers window functions, recursive CTEs, ML-specific patterns, and 15 questions actually asked at Amazon, Meta, Uber, and more.
How Amazon, Coupang, and Walmart Use AI — And What It Means for Your Interview
These three companies run some of the most sophisticated applied AI systems outside dedicated AI labs. Understanding what they actually built — Rufus, demand forecasting, supply chain optimization, fraud detection — directly changes how you answer their interview questions.
AI Skills Roadmap 2026 — What to Learn, in What Order, for AI Engineering Jobs
An opinionated, ordered roadmap for getting into AI engineering — not a list of 40 resources. Covers 4 entry points (from SWE, data science, fresh grad), skills by ROI priority, what to skip, 3 portfolio projects that actually get interviews, and realistic timelines.
How Amazon Uses AI — And How That Knowledge Helps in Amazon Interviews
Amazon hired more AI engineers in 2024 than the previous 3 years combined. Their AI stack spans Rufus, recommendations, supply chain optimization, Alexa, and AWS AI services. Understanding what they built changes how you answer their interviews — technically and through the Leadership Principles lens.
Model Context Protocol Explained — How MCP Actually Works (and Where It Breaks)
MCP is the emerging standard for connecting AI agents to tools and data. This deep-dive covers the four primitives, transport layer, security failure modes, and why the sampling primitive changes the security model entirely.
RAG vs Fine-Tuning for Indian Startups — The Real Cost Math in 2026
The question isn't RAG or fine-tuning. It's which one at which scale. This covers the decision framework, the optimization order that cuts costs 60% before architecture matters, India GPU pricing (E2E Networks vs AWS Mumbai), and the three-stage deployment path.
ML System Design Interview Questions — The 5-Component Framework
ML system design is a distinct interview format — not software design with a model bolted on. This covers the 5-component answer structure, the 5 problems that appear most (recommendation, fraud detection, demand forecasting, search ranking, content moderation), and company-specific patterns at Amazon, Flipkart, Coupang, and Uber.
Machine Learning Interview Questions — What You Memorize vs What Gets Asked
ML interviewers don't want definitions. They design questions where the right answer requires reasoning about tradeoffs, failure modes, and production realities. This covers the core questions that filter candidates, model evaluation, deep learning fundamentals, and the questions that separate strong candidates.
Data Science Interview Questions — Statistics, SQL, ML, and Case Studies
DS interviews span 5 domains and candidates systematically over-prepare one while neglecting the others. This covers the statistics and probability questions that filter 60% of candidates, the SQL patterns data scientists actually get asked, product case study structure, and a 3-week prep plan.
Product Company Interview Questions India — Flipkart, Coupang, Razorpay, Microsoft
Each top Indian product company has a distinct interview style — and prepping the same way for all of them is why engineers fail. This covers the Flipkart machine coding round, Coupang's near-FAANG algorithm difficulty, Razorpay's distributed systems depth, and salary ranges with negotiation context.
AI Engineer vs ML Engineer vs Data Scientist — Which Path in 2026
The job titles are used interchangeably but the interview processes are completely different — prepping for the wrong one costs 6-8 weeks. This covers what each role actually builds, the skill gap, how interviews differ, salary ranges (India + US), and the 6-month transition plan from SWE.
LangChain vs LangGraph vs AutoGen vs CrewAI — Which Agent Framework to Use
90% of production agentic systems are built on three frameworks — and each wins for a specific architectural reason. This covers why LangGraph dominates stateful workflows, where AutoGen beats everything for multi-agent debate, CrewAI's ceiling problem, and the framework architecture questions now asked in senior interviews.
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