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 it
Every code example was run before it was printed, and every number was measured, not copied from a blog. Some of what the runs found:
- 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.
- 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.
- Hybrid search made no measurable difference on its own. A reranker did the work, and the book shows the control experiment that proves it.
- An LLM judge that passes everything scored 85% agreement with humans while catching zero failures. You'll learn the statistic that exposes it.
- An agent that's 95% reliable per step finishes a 20-step task only 36% of the time.
- Merging a fine-tune into 4-bit weights silently erased it, scoring 0%. Merging into full-precision weights kept it at 99%.
What's inside
- 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.
- 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.
- 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.
- 85 diagrams and chartsfor the ideas that are hard to picture.
- Every modulehas a skip test, a build, a pass/fail checkpoint, common traps and interview questions.
- Runs for ₹0:free model tiers and local models cover almost every example, with no card needed.
- 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.
- 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.
- 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.
Who it's for
Students 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 for
Anyone 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:
- a 361-page PDF with a clickable contents page, a list of figures and bookmarks for every section;
- 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.
Current as of October 2026.
Price: ₹199