ABOUT THE PAGE
Everything 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.
- Python Core for AI: NumPy, Pandas, OOP, File I/O
- LLM APIs & Prompting: Anthropic, OpenAI, embeddings
- GenAI Libraries: LangChain, LlamaIndex, ChromaDB, Hugging Face, LangGraph, CrewAI
- RAG & Memory Systems: chunking, hybrid search, HyDE, memory architectures
- Deployment & Production: Streamlit, FastAPI, evaluation, observability
- 3 portfolio projects: RAG Research Assistant, Autonomous Agent, Multi-Agent Content Studio
📘 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.
- 13 chapters: ingestion, embeddings, retrieval, reranking, grounding, evaluation, agentic RAG, GraphRAG, multimodal, text-to-SQL, security, production
- 4 appendices: decision cheat-sheets, tooling map, reference architectures, anti-patterns
- Runnable, version-pinned code for every chapter
- Full capstone: a tested, hardened RAG service plus an architecture decision record
- Runs on free tooling: one free API key, everything else local. Zero cost to run.
📙 Guide 3: Gen AI Engineer Interview Kit (2026 Edition) (Get Hired)
6 files covering everything from preparation to offer.
- 12-week roadmap with free verified resources and mini projects
- Core concepts at interview depth: transformers, RAG, agents, LoRA/QLoRA, RAGAS, OWASP LLM security, with strong vs weak answer examples
- System design guide with full architectures and 3 worked examples, for the round most candidates fail
- 80+ interview Q&As with production-grade answers
- 5 portfolio projects with starter code, including an MCP server, an evaluation framework and QLoRA fine-tuning
- Job search playbook: ATS resume template, LinkedIn optimization, recruiter outreach templates, salary benchmarks, negotiation scripts
🎯 Who this is for
- Software engineers and developers moving into AI engineering roles
- You need basic Python. No prior AI experience required.
- Target roles: GenAI Engineer, Applied AI Engineer, LLM Engineer, AI Agent Engineer
- ❌ Not for you if you want no-code tools or a vendor-logo tour
💼 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
- 3 complete packages, each a separate ZIP file you download instantly
- 11 portfolio projects across all three packages
- Runnable code, verified against current library versions
And the price line at the bottom:
₹̶5̶9̶7̶ ₹299 · Save 50% · instant download · 3 ZIP packages (guides + code)