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The 2026–27 edition of the Gen AI Engineer Interview Kit, rebuilt from the ground up as a three-volume book for GenAI Engineer, Applied AI Engineer, LLM Engineer and AI Agent Engineer interviews. What's Inside Volume 1 - The Question Bank166 interview questions on transformers, embeddings, prompting, RAG, agents and MCP, evaluation, fine-tuning, inference, security and multimodal systems. Every answer follows the same seven-part structure: what the interviewer is really testing, a 30-second answer you can say aloud, a deep answer, follow-up questions, the weak answer that sounds right but fails, and what a senior candidate adds. Volume 2 - The RoundsEverything beyond the Q&amp;A: • Estimation: KV-cache size, GPU fit, serving cost • 14 production-debugging scenarios • 16 full system-design cases • 24 live-coding solutions, every one tested • The human rounds: project deep-dive, customer simulation, eval design, AI-assisted coding • 3 scripted mock interview loops • Job-search, resume and negotiation playbook Volume 3 - The Builds8 portfolio projects you build, run and defend in an interview: • A tiny GPT from scratch • A cited RAG take-home with an evaluation set • An LLM-as-judge eval harness • A multi-provider LLM gateway • An MCP tool-using agent • Text-to-SQL • A prompt-injection red-team lab • QLoRA fine-tuning on an 8 GB GPU Each comes with working code, tests, measured results and resume bullets. Everything runs on free-tier APIs. Why This One • 240 questions answered in depth, plus 448 follow-ups • Every code example was run before it went into the book • Every number was measured or computed, never guessed • Each question is mapped to the round where it comes up • Company-specific questions are marked as reported, with sources • Models, prices and limits were checked in October 2026, and dated so you know exactly how current they are Who This Is For Engineers with intermediate Python who have built something with LLMs, or are learning to, and have interviews coming up. Includes 7-day, 30-day and 8-week study plans. New to AI? Start with <a target="_blank" href="https://superprofile.bio/vp/gen-ai--zero-to-hero">Gen AI Zero To Hero</a>, then use this book to prepare for interviews. Format • 3 PDFs: about 970 pages, 71 figures, a full contents page and a bookmark for every question • Companion folder with all code, data and tests • The 6 first-edition guides, included • Delivered instantly after purchase Already Bought the Kit? This edition is a free upgrade for you. Download it again from your purchase link.

Gen AI Engineer Interview Kit 

The complete Interview preparation resource for GenAI Engineer

From your first line of Python to an offer as a GenAI engineer.Everyone can build a RAG chatbot now. That's exactly why it no longer gets anyone hired.The engineers companies are actually looking for can do four things the free roadmaps skip: prove their system works, stop it being abused, know what it costs per user, and deploy it where companies really run software. This book is built around those four skills.It's a build list, not a reading list. You build one real system, a document assistant for an Indian insurance company, and upgrade it module after module until it has retrieval, agents, evals, security and cloud deployment behind it. By the end you have one serious portfolio project instead of fifteen tutorials.What's different about itEvery code example was run before it was printed, and every number was measured, not copied from a blog. Some of what the runs found:<ul><li>Search leaked between customers: without permission filters, 168 of 480 results belonged to someone else. The book shows the fix and the test that catches it.</li><li>A real fine-tune of a 4B model beat a well-written prompt by just 2 claims in 100. The same chapter shows why the fine-tune can still be worth it.</li><li>Hybrid search made no measurable difference on its own. A reranker did the work, and the book shows the control experiment that proves it.</li><li>An LLM judge that passes everything scored 85% agreement with humans while catching zero failures. You'll learn the statistic that exposes it.</li><li>An agent that's 95% reliable per step finishes a 20-step task only 36% of the time.</li><li>Merging a fine-tune into 4-bit weights silently erased it, scoring 0%. Merging into full-precision weights kept it at 99%.</li></ul>What's inside<ul><li>16 modules:Python and SQL foundations, how LLMs work (attention, explained with code), APIs and cost, context engineering, backend and multi-tenancy, production RAG, agents, MCP, evals, security and red teaming, Azure/AWS/GCP, fine-tuning, inference and GPU costs, voice, AI coding tools, and a full job-search module.</li><li>The frameworks jobs ask for:you build each piece by hand first, then in LangChain, LangGraph and LlamaIndex, and see what their defaults hide. MCP servers, Ragas and DeepEval are covered too.</li><li>Real runs, not toy demos:a QLoRA fine-tune and a vLLM server measured on a laptop GPU, a voice agent measured stage by stage, and real PDF and OCR parsing.</li><li>85 diagrams and chartsfor the ideas that are hard to picture.</li><li>Every modulehas a skip test, a build, a pass/fail checkpoint, common traps and interview questions.</li><li>Runs for ₹0:free model tiers and local models cover almost every example, with no card needed.</li><li>India-specific guidance:services-company and campus hiring, DPDP and RBI compliance, Hindi, Hinglish and Malayalam costs, CTC and offer negotiation, and which certification actually matters.</li><li>Interview preparation:two fully worked system-design answers plus short answers for six more, and a seven-day plan for when an interview is already booked.</li><li>Four appendices:a glossary, the 2026 tool stack, every checklist in one place, and week-by-week schedules for six starting points, including a 16-week student track and a part-time plan.</li></ul>Who it's forStudents and developers who want a GenAI engineering role and are tired of tutorials that stop at the demo. It works whether you're starting from zero or already write code: each module's skip test tells you what to skip.Who it's not forAnyone looking for a six-week shortcut. The book is honest about timelines: about 20 weeks if you already code, 7–8 months if you're starting from scratch.You get:<ul><li>a 361-page PDF with a clickable contents page, a list of figures and bookmarks for every section;</li><li>the free Visual Interview Notes: a 22-page PDF that turns each module into one page to draw from memory, with the answers to say aloud.</li></ul>Current as of October 2026.Price: ₹199

Gen AI: Zero to Hero

From your first line of Python to an offer as a GenAI engineer.

Data Analyst: Zero to Job takes you from no experience to job-ready. You work through 27 modules on one realistic Indian business: Basketwala, an online grocery company, whose data is deliberately messy, the way real data is.You learn Excel, SQL, Python, statistics and Power BI the way analysts use them at work. Then you turn that into a portfolio, interview answers and a job search plan for India.Every one of the 326 code examples was run before it went into the course, and every output shown comes from a real run. You generate the same dataset yourself, so if your answer differs from the course's, the mistake is yours to find, never the course's.For freshers, career switchers, and anyone in MIS, BPO, commerce or engineering roles. <h2>Course outcomes</h2><ul><li>Clean messy real-world data in Excel, SQL and Python: duplicates, missing values, mixed date formats, inconsistent city names, refunds.</li><li>Write SQL from a first SELECT through joins, CTEs and window functions, and build cohort and funnel analyses.</li><li>Use Excel the way analysts do: lookups (with fallbacks for older Excel), pivot tables, Power Query, and lakh and crore formatting.</li><li>Analyse and chart data in Python with pandas.</li><li>Use statistics with confidence: spot averages that mislead, read confidence intervals, use regression, run A/B tests, and know when a number proves nothing.</li><li>Build a Power BI data model, write DAX measures and design a dashboard a manager can use.</li><li>Turn a vague business question into a number someone trusts, and explain it to a stakeholder.</li><li>Complete capstone projects and a mock take-home assignment that form your portfolio.</li><li>Prepare for SQL tests, case rounds and stakeholder questions; write a resume and LinkedIn profile that show real work; run a job search and read an offer letter in India.</li><li>Start your first analyst job with a plan for your first 90 days.</li></ul>

Data Analyst Zero to Job

Data Analyst: Zero to Job takes you from no experience to job-ready

ABOUT THE PAGEEverything you need to go from Python developer to hired GenAI engineer: learn the stack, build production RAG, and crack the interview. Three complete guides, one path.🧭 The path: Learn → Build → Get Hired📗 Guide 1: Python for Generative AI Complete Guide (Learn) 6 phases, 22 modules, 340+ pages, from Python basics to production AI apps.<ul><li>Python Core for AI: NumPy, Pandas, OOP, File I/O</li><li>LLM APIs &amp; Prompting: Anthropic, OpenAI, embeddings</li><li>GenAI Libraries: LangChain, LlamaIndex, ChromaDB, Hugging Face, LangGraph, CrewAI</li><li>RAG &amp; Memory Systems: chunking, hybrid search, HyDE, memory architectures</li><li>Deployment &amp; Production: Streamlit, FastAPI, evaluation, observability</li><li>3 portfolio projects: RAG Research Assistant, Autonomous Agent, Multi-Agent Content Studio</li></ul>📘 Guide 2: RAG From Scratch → Production (Build) Build retrieval systems you can actually ship, not just notebook demos. You build every piece by hand first, then rebuild it with the framework, so nothing is a black box.<ul><li>13 chapters: ingestion, embeddings, retrieval, reranking, grounding, evaluation, agentic RAG, GraphRAG, multimodal, text-to-SQL, security, production</li><li>4 appendices: decision cheat-sheets, tooling map, reference architectures, anti-patterns</li><li>Runnable, version-pinned code for every chapter</li><li>Full capstone: a tested, hardened RAG service plus an architecture decision record</li><li>Runs on free tooling: one free API key, everything else local. Zero cost to run.</li></ul>📙 Guide 3: Gen AI Engineer Interview Kit (2026 Edition) (Get Hired) 6 files covering everything from preparation to offer.<ul><li>12-week roadmap with free verified resources and mini projects</li><li>Core concepts at interview depth: transformers, RAG, agents, LoRA/QLoRA, RAGAS, OWASP LLM security, with strong vs weak answer examples</li><li>System design guide with full architectures and 3 worked examples, for the round most candidates fail</li><li>80+ interview Q&amp;As with production-grade answers</li><li>5 portfolio projects with starter code, including an MCP server, an evaluation framework and QLoRA fine-tuning</li><li>Job search playbook: ATS resume template, LinkedIn optimization, recruiter outreach templates, salary benchmarks, negotiation scripts</li></ul>🎯 Who this is for<ul><li>Software engineers and developers moving into AI engineering roles</li><li>You need basic Python. No prior AI experience required.</li><li>Target roles: GenAI Engineer, Applied AI Engineer, LLM Engineer, AI Agent Engineer</li><li>❌ Not for you if you want no-code tools or a vendor-logo tour</li></ul>💼 Why now AI engineering roles in India range from 8 LPA at entry level to 90 LPA at senior level at AI-first companies. Remote global roles for India-based engineers pay $70K–$160K USD. ✨ What you get<ul><li>3 complete packages, each a separate ZIP file you download instantly</li><li>11 portfolio projects across all three packages</li><li>Runnable code, verified against current library versions</li></ul>And the price line at the bottom:₹̶5̶9̶7̶ ₹299 · Save 50% · instant download · 3 ZIP packages (guides + code)

GenAI Job-Ready Bundle

Everything you need to go from Python developer to hired GenAI engineer

Build retrieval systems you can actually ship, not just notebook demos.What it is<ul><li>A hands-on guide that takes you from "my RAG demo works" to "I can ship and defend a production RAG system."</li><li>You build every piece by hand first, then rebuild it with the framework, so nothing is a black box.</li><li>Organized as a&nbsp;complexity ladder: learn the cheap wins first, the exotic stuff last, and when to skip a rung.</li></ul>Who it's for<ul><li>Developers comfortable with&nbsp;Python + APIs.</li><li>Anyone stuck at "the demo fell apart on real users, real data, or real cost."</li><li>❌ Not for you if you want no-code drag-and-drop or a vendor-logo tour.</li></ul>What's inside<ul><li>📘13 chapters: ingestion, embeddings, retrieval, reranking, grounding, evaluation, agentic, GraphRAG, multimodal, text-to-SQL, security, production.</li><li>🧭4 appendices: decision cheat-sheets, tooling map, reference architectures, anti-patterns.</li><li>💻Runnable code&nbsp;for every chapter, version-pinned, built from scratch.</li><li>🚀Full capstone project: a tested, hardened RAG service plus an architecture decision record.</li><li>🔑 Runs on&nbsp;free tooling: one free API key, everything else local. Zero cost to run.</li></ul>₹̶2̶9̶9̶&nbsp; ₹199 · instant download · book + code + capstone

RAG From Scratch → Production

Build retrieval systems you can actually ship, not just notebook demos.

Stop building to-do-list clones. Ship the 5 projects that actually get GenAI interviews.Five complete, working applications - RAG search, an autonomous agent, a real-time voice AI, a browser-testing agent, and a full-stack AI SaaS - each with real source code, a step-by-step build guide, and real evaluation numbers. Built entirely on free-tier APIs (Groq, Google Gemini free tier, local models), so you pay ₹0 to build and run every one of them.<hr><h2>What you get</h2><ul><li>5 complete codebases, zipped and fully self-contained - unzip, install, run</li><li>5 build guides(<code>GUIDE.md</code>) - not tutorials, teaching docs written from the actual code, explainingwhyevery architectural decision was made</li><li>5 READMEs&nbsp;with exact run commands,<code>.env.example</code>files, and one-command local setup</li><li>Real eval numbers&nbsp;for every project - no invented benchmarks, every metric is reproducible</li><li>A portfolio overview guide&nbsp;mapping all 5 projects' stacks and the skills each one proves</li><li>Zero-cost to build: every model call routes through Groq's and Gemini's free tiers, plus local CPU models - no credit card needed anywhere</li></ul><h2>The 5 projects</h2>1. DocuMind - RAG Document Intelligence&nbsp;Ask questions over your own PDFs and get answers with clickable page citations. Ships the retrieval pattern most tutorials skip: dense + BM25 hybrid search -&gt; RRF fusion -&gt; cross-encoder reranking. Hit@5 96% -&gt; 100%, MRR +0.138 measured before/after hybrid+rerank.<code>React · FastAPI · Qdrant · Gemini</code>2. TaskPilot - Autonomous Research &amp; Action Agent&nbsp;Give it a goal; it plans, searches, reads, critiques its own draft, and acts - pausing for your approval before anything risky. Includes a custom-built MCP server and durable checkpointing that survives a server restart mid-task.<code>LangGraph · MCP · Groq · React</code>3. VoiceCoach - Real-Time Voice AI Interview Coach&nbsp;Talk to it. It interviews you out loud, scores your answers against a rubric, and coaches you back - with true barge-in (interrupt it mid-sentence like a real conversation). p50 end-to-end voice latency: 3.6 seconds. STT word-error-rate: 5.7%.<code>WebSocket · Whisper · Kokoro TTS · Groq</code>4. AutoQA - Autonomous Browser QA Agent&nbsp;Describe a test in plain English; it drives a real Chromium browser, checks the flow, and files a structured bug report with screenshot evidence - with safety rails (allowlists, human approval before form submits) built in from day one.<code>Playwright · Gemini Vision · LangGraph</code>5. SpendLens - Multimodal Expense SaaS (Capstone)&nbsp;Upload a receipt photo, get validated structured data with confidence scores, correct it in a review queue, then chat with your spending - grounded, cited, multi-tenant. Includes a CI pipeline that blocks bad pull requests on quality regressions.&nbsp;<code>Next.js 16 · Supabase · Clerk · Vercel AI SDK</code><h2>Who this is for</h2><ul><li>Developers who want&nbsp;5 resume-ready GenAI projects&nbsp;without spending weeks figuring out architecture from scratch</li><li>Anyone prepping for AI engineer / agent engineer interviews who wants concrete talking points backed by real numbers, not buzzwords</li><li>Students who want tolearn by reading real code, not another "call the OpenAI API in 10 lines" tutorial</li></ul><h2>Why this is different</h2>Most GenAI tutorials stop at "call the model, print the response." Every project here handles what actually separates a demo from a portfolio piece: hybrid retrieval instead of naive top-k, human-approval gates before an agent writes anything, safety rails before letting an LLM drive a browser, confidence-gated human review instead of trusting a vision model blindly, and an eval suite with numbers you can reproduce yourself.<h2>What you need</h2>Free accounts only - Groq, Google AI Studio (Gemini), and (for project 5) Clerk + Supabase. No paid API, no GPU, no credit card.<h2>Price: ₹199</h2>One-time purchase. Instant download - 5 zips + all guides

GenAI Portfolio Kit - 5 Production-Grade Projects (Source + Build Guides)

🚀 5 GenAI projects. Real code. Real eval numbers. ₹0 to build

A complete learning path from Python basics to building production AI apps - everything in one place. 📚 What's inside - 6 phases and 22 module, 340+ Pages:• Phase 1&nbsp;- Python Core for AI (NumPy, Pandas, OOP, File I/O) • Phase 2&nbsp;- LLM APIs &amp; Prompting (Anthropic, OpenAI, embeddings) • Phase 3&nbsp;- GenAI Libraries (LangChain, LlamaIndex, ChromaDB, Hugging Face, LangGraph, CrewAI) • Phase 4&nbsp;- RAG &amp; Memory Systems (chunking, hybrid search, HyDE, memory architectures) • Phase 5&nbsp;- Deployment &amp; Production (Streamlit, FastAPI, evaluation, observability) • Phase 6&nbsp;- 3 End-to-End Portfolio Projects(RAG Research Assistant, Autonomous Agent, Multi-Agent Content Studio) What makes this different:• 340+ pages of practical, structured content. • Every code example is verified against the latest library versions. • Three portfolio projects.

Python for Generative AI Complete Guide

A complete learning path from Python basics to building production AI apps

Used by engineers preparing for Google, Meta, Amazon, Microsoft, and top AI startups.Most ML candidates study for months and still freeze in the interview. Not because they lack knowledge, but because they have no structured plan, no complete reference, and no idea what interviewers actually test.This kit fixes all three.What's inside:📘 ML Question Bank135 questions across 8 topics: fundamentals, algorithms, deep learning, LLMs, MLOps, system design, math, and coding. Every question answered in full. No placeholders. No shortcuts.🗺️ 12-Week Interview RoadmapA day-by-day study plan across 5 phases, from building your foundation to full mock interview mode. You will always know exactly what to do next.⚡ Complete ML Algorithm Guide17 algorithms explained with formulas, Python code, pros and cons, and the exact interview angle for each one.📊 ML Cheat SheetA dense technical reference covering Deep Learning, LLMs, RAG, LoRA, RLHF, MLOps, and more. Built for last-week revision, not casual reading.📋 How To Use This BundleA one-page guide showing exactly which document to open first, when to use each one, and how to get the maximum value from all four in the shortest time.This is not a course. There are no videos to watch or assignments to submit. Just everything you need, structured the right way, ready to use today.₹199 — One-time payment. Yours forever.→ Buy now and get instant access.

The Complete ML Interview Kit

Build production-grade AI agents. Ship something real.Most AI agent tutorials get you to a demo. This gets you to production.14 chapters. Every code block runs. Every warning comes from something that broke in real deployments. No fluff, no theory without code — just the patterns, tools, and architecture decisions that separate agents that work from agents that look good in a notebook.What's inside:<div><ul><li class="font-claude-response-body whitespace-normal break-words pl-2">When to build an agent vs a simpler system (and the 5-question test to decide)</li><li class="font-claude-response-body whitespace-normal break-words pl-2">Your first working agent in under an hour</li><li class="font-claude-response-body whitespace-normal break-words pl-2">ReAct, Plan-and-Execute, Reflection — full code for each pattern</li><li class="font-claude-response-body whitespace-normal break-words pl-2">Multi-agent systems, memory, and RAG — done right</li><li class="font-claude-response-body whitespace-normal break-words pl-2">Reliability, observability, security — the part most tutorials skip entirely</li><li class="font-claude-response-body whitespace-normal break-words pl-2">5 complete ready-to-adapt builds: customer support, research, data analysis, code review, voice agent</li></ul></div>Built for developers who know Python and want to ship — not researchers who want to read.₹199 · 7 files · Instant download

GenAI Agents Cookbook

Build production-grade AI agents. Ship something real.

Master SQL from beginner to interview-ready with a practical, real-world focused guide designed for developers, analysts, and data professionals. What’s Inside:<ul><li>13 structured chapters from basics to advanced</li><li>JOINs, CTEs, Subqueries &amp; Window Functions</li><li>35+ SQL interview questions and answers</li><li>Real-world scenarios in E-Commerce, HR &amp; Analytics</li><li>Practice exercises with complete solutions</li><li>Query optimization, indexing &amp; performance tips</li><li>Ultimate SQL cheat sheet for quick revision</li></ul>Perfect For:<ul><li>Beginners learning SQL from scratch</li><li>Interview preparation</li><li>Data Analysts &amp; Data Scientists</li><li>Backend &amp; Full-Stack Developers</li></ul>Built to help you understand how SQL works in real-world applications - not just theory.

The Complete SQL Guide - Premium Edition

Master SQL from beginner to interview-ready

<h3>A Hands-On Guide to Building Modern AI Systems</h3>Stop using AI like a black box. Learn how modern AI actually works - from the mathematical foundations to building real-world Generative AI systems.This book is a practical, beginner-friendly yet technically deep guide designed for developers, engineers, students, and curious builders who want to truly understand modern AI instead of just calling APIs.Inside this guide, you’ll learn:✓ How machine learning actually learns from data ✓ Neural networks built completely from scratch ✓ Backpropagation, gradient descent, and optimization explained visually ✓ Tokenization, embeddings, and how language becomes numbers ✓ Attention mechanisms and Transformers from first principles ✓ How GPT-style models work internally ✓ Fine-tuning, LoRA, RLHF, and modern LLM training workflows ✓ RAG pipelines, evaluation systems, and AI application architecture ✓ Production AI engineering concepts used in real-world systems ✓ Practical Python implementations throughout the bookThis is not another shallow “AI overview” ebook. You’ll build intuition step-by-step while also learning the engineering concepts behind today’s most powerful AI systems.<h2>What Makes This Different?</h2>Unlike most AI content online that skips straight to frameworks and APIs, this guide focuses on deep understanding first - so you can confidently build, debug, and improve AI systems yourself.The book combines:<ul><li>First-principles explanations</li><li>Real code implementations</li><li>Practical engineering insights</li><li>Modern Generative AI workflows</li><li>Beginner-friendly teaching style</li></ul><h2>Perfect For</h2><ul><li>Developers transitioning into AI/ML</li><li>Software engineers exploring Generative AI</li><li>Students learning modern AI systems</li><li>AI enthusiasts wanting deeper understanding</li><li>Builders creating LLM applications</li><li>Anyone tired of surface-level tutorials</li></ul><h2>Includes</h2><ul><li>6 structured parts</li><li>Real Python code examples</li><li>Transformer implementations</li><li>Fine-tuning workflows</li><li>Evaluation pipelines</li><li>Production AI concepts</li><li>Visual explanations &amp; diagrams</li></ul>Whether you want to become an AI engineer, build GenAI products, or simply understand how systems like ChatGPT actually work - this guide gives you the foundations to do it properly. 🚨ACCESS THE 6 PART GUIDE IN ZIP FILE 👇

Generative AI From First Principles

A complete hands-on guide to understanding how modern AI systems work.

✓ ATS-Optimized Resume Templates ✓ Cover Letter Templates ✓ LinkedIn Optimization Checklist ✓ Skills Cheat Sheet 2026 ✓ 150 Interview Questions + Model Answers ✓ Data Science Roadmap ✓ 15 Portfolio Project Briefs ✓ GitHub &amp; Portfolio Setup Guide ✓ 30-Day Job Search Action Plan + Tracker ✓ 8 Networking &amp; Outreach Templates ✓ ML Concepts Quick Reference Card ✓ Salary Negotiation Guide + Scripts ✓ ATS Keyword Bank

📦 Data Science Job Kit 2026 

Built for 2026 - GenAI skills, MLOps expectations, real market rates.

​Here's what's inside (10 resources): ✓ ATS-Optimised Resume Template ✓ GenAI Role Map &amp; Skills Matrix✓ 450+ ATS Keywords (GenAI Edition)✓ LinkedIn Optimization Checklist✓ Full Learning Roadmap (Detailed)✓ 60 Interview Q&amp;As with Full Answers✓ 3 System Design Templates✓ Salary Scripts + India Benchmarks✓ 8 Cold Outreach Templates✓ 30-Day Job Search Action Plan Everything you need - from zero to offer. Download the Zip file now!

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