AI Launchpad - Flagship Program Now Enrolling

Helping Senior Developers Transition Into Generative AI

Engineers Enrolled
1,200+
Senior devs who've already made the transition
Avg. Salary Uplift
↑34%
Within 6 months of completing the program
Program Length
5 months
Structured, project-based, async-friendly
View curriculum
The Program

What you'll learn & build

5 months. 10 production artifacts. Every week ends with something shipped to GitHub.

Month 1 - Python, ML Fundamentals & LangChain Engineering
Week 0Onboarding
Learning Methodology & Setup
  • Active Recall & Spaced Repetition techniques
  • Development environment setup
  • Learning system before day one
Tools
VS Code Git GitHub
Week 1
Python for AI Engineers
  • OOP, data structures, exception handling
  • HTTP requests, JSON, SQLite basics
  • 35 coding problems shipped to GitHub
  • Two OOP capstone projects deployed
Tools
Python 3 Jupyter FastAPI GitHub
Week 2
Machine Learning Foundations
  • End-to-end ML pipeline: data → train → serve
  • Decision Trees, train/test split, accuracy
  • Bridge from classical ML to LLMs
Tools
Scikit-learn Pandas NumPy Matplotlib
Week 3
LLM Fundamentals
  • Tokens, context windows, cost optimisation
  • Prompting: Zero-Shot, Few-Shot, Chain-of-Thought
  • Structured outputs, tool calling, embeddings
  • Attention mechanism & Transformer architecture
Tools
OpenAI API Claude API Gemini API
Week 4Build
LangChain Engineering - Chains & Agents
  • Prompt templates, sequential & parallel chaining
  • ReAct agents with custom tools
  • Customer Support Triage System (P3)
  • Sales Research ReAct Agent (P4)
Tools
LangChain LangSmith Promptfoo Tavily
Month 2 - RAG Systems, AI Agents & Full-Stack AI
Week 1
RAG Foundations
  • Vector embeddings & cosine similarity
  • Ingestion pipeline: load → chunk → embed → store
  • Conversational RAG with persistent chat history
Tools
LangChain FAISS Chroma LlamaIndex
Week 2Build
Advanced RAG & Production Retrieval
  • Hybrid search: vector + BM25 keyword
  • Multi-query RAG, reranking with Cohere
  • Multi-modal RAG: PDFs, images, tables (P6)
  • RAG evaluation with RAGAS
Tools
Pinecone Weaviate Cohere RAGAS Unstructured.io
Week 3
AI Agents & LangGraph
  • StateGraph, checkpointing, human-in-the-loop
  • Reflection agents & self-correction loops
  • Multi-agent supervisor architecture
  • LangSmith tracing for full observability
Tools
LangGraph LangSmith CrewAI Mem0
Week 4Build
Capstone - Perplexity Clone (P7)
  • React frontend + FastAPI backend + LangGraph agent
  • Real-time web search & cited answer synthesis
  • Token-by-token streaming via SSE
  • Fully deployed - demo to employers
Tools
React FastAPI LangGraph Docker Vercel
Month 3 - Production AI Systems (The Six-Figure RAG)
Week 1
Database Design & Auth
  • ERD design for multi-tenant AI apps
  • pgvector + TSVector for hybrid search in PostgreSQL
  • Next.js App Router + Clerk authentication
Tools
PostgreSQL Supabase pgvector Next.js Clerk
Week 2
Async RAG Ingestion Pipeline
  • AWS S3 pre-signed URL uploads
  • Celery + Redis async task queue
  • Multi-modal parsing: PDFs, images, tables, URLs
  • Real-time status updates via short-polling
Tools
AWS S3 Celery Redis Unstructured.io
Week 3
Retrieval, Agents & Guardrails
  • Hybrid search with pgvector + TSVector + RRF
  • LangGraph 1.0 multi-agent supervisor
  • Input/output guardrails + LLM-as-a-Judge
  • RAGAS evaluation as a quality gate
Tools
LangGraph Guardrails AI NeMo RAGAS
Week 4Build
Logging, Monitoring & Cloud Deploy (P8)
  • Structured logging & middleware
  • Docker + AWS ECS Fargate deployment
  • CloudFront HTTPS + Vercel frontend
  • Live URL to share with recruiters
Tools
Docker AWS ECS CloudFront GitHub Actions
Month 4 - Project Studio: Build for Your Career
GenAI Engineering
Advanced RAG & Data Pipelines
  • GraphRAG with Neo4j or FalkorDB
  • GB-scale RAG with async cloud ingestion
  • WhatsApp Bot with multimodal RAG
  • YouTube summariser with email delivery
Tools
Neo4j LlamaIndex Azure AI Search AWS OpenSearch
Agentic AI
Autonomous Agent Projects
  • Deep Research Agent with citations
  • Advanced SQL Agent on live databases
  • LinkedIn Automation Agent
  • Collaborative multi-agent systems
Tools
LangGraph CrewAI AutoGen Playwright
MCP Servers
Model Context Protocol Projects
  • Email Copilot MCP (Gmail read/draft/send)
  • Salary Prediction MCP wrapping an ML model
  • Health Monitor MCP with IoT sensor data
  • Universal MCP Client (your own Claude Desktop)
Tools
MCP SDK FastMCP GitHub MCP Playwright MCP
Month 5 - Career Acceleration & Job Placement
LinkedIn
LinkedIn Optimisation
  • Headline engineering (220 chars that get clicks)
  • About section rewritten around outcomes
  • Featured section showcasing your builds
  • Reviewed & signed off by AI Launchpad team
Outcome
Published profile Content strategy
Resume & Interviews
Résumé & Mock Interviews
  • ATS-optimised single-page résumé
  • Tailored variants for different AI roles
  • 2 full mock interviews with written feedback
  • Salary negotiation roleplay
Outcome
2 résumé variants Interview-ready
PlacementSupport
Job Assistance
  • Target company list built with you
  • Warm intros from AI Launchpad network
  • Offer evaluation & compensation review
  • 30-day post-placement check-in
Outcome
Active pipeline Offers received

What GenAI can help you become

I'm a
Backend Engineer
I'm a
Frontend Engineer
I'm a
Full Stack Engineer
I'm a
Product Manager
You could be a
LLM Backend Engineer
Who Built a
Competitive Question Generator
An innovative AI Chrome extension designed to generate competitive exam questions specifically tailored for UPSC exams, helping aspirants practise smarter with AI-generated content.
Turns scattered inputs into clarity Conversations that guide Output that's structured & ready
You could be an
AI Product Engineer
Who Built a
LLD: Smart Sales Driver with GenAI
A low-level design for an AI-powered sales driver that intelligently surfaces opportunities, automates outreach, and guides reps through deal pipelines.
Turns scattered inputs into clarity Conversations that guide Output that's structured & ready
You could be a
Full Stack AI Engineer
AI
Who Built a
GenAI Powered Medical Support System
A full-stack product that takes medical data as input to help doctors with a prior knowledge base - enabling faster, AI-assisted clinical decision-making.
Turns scattered inputs into clarity Conversations that guide Output that's structured & ready
You could be an
AI Product Manager
Who Built an
AI Marketing Platform for Online Coaches
An AI agent that researches trending topics and generates relevant, engagement-driven LinkedIn content tailored to an online coach's audience and brand voice.
Turns scattered inputs into clarity Conversations that guide Output that's structured & ready

What's getting in the way right now?

Right now, it feels like
I don't have time
Right now, it feels like
I feel stuck and overwhelmed
Right now, it feels like
I'm not sure what to focus on
Right now, it feels like
I'm not seeing results
What if someone had already mapped the path for you?
Learn more
Choose what feels familiar.
AI PATH for you
Learn more
What if your background already pointed to the answer?
Learn more
What if every session ended with something you could show?
Learn more
Alumni Story
From 22 LPA to Rs5,20,000/month - in 5 months
Tarun was a Frontend Engineer at Freshworks. Stable job, great culture, zero complaints. But AI was replacing frontend work, layoffs were real, and his resume was stagnating. In 5 months he landed a US-based remote role as a Gen AI Engineer - 30 applications, 7 interviews, 3 offers.
5 mo
Time to transition
3 offers
From 30 applications
+40 LPA
Salary increase
👨‍💻
Tarun
Frontend Engineer → Gen AI Engineer, US Remote
28 min
Testimonials

What our alumni say

"
Her guidance and accountability helped me stay consistent, focused, and structured throughout my Generative AI learning journey.
My experience with Sowmya has been extremely valuable and impactful. Her guidance and accountability helped me stay consistent, focused, and structured throughout my Generative AI learning journey. Through this process, I was able to complete courses across Prompt Engineering, LangChain, LangGraph, Vector Databases, Knowledge Graphs, MCP, Fine-tuning, RLHF, LLMOps, Agentic AI, Pydantic, and GenAI evaluation/debugging. More importantly, the mentorship helped me connect these concepts practically and apply them in my current company.
👨‍💼
Sarath V
Senior Software Engineer · 12+ Years Experience
LinkedIn
Short outcome badge
"Her guidance and accountability helped me stay consistent throughout my GenAI journey."
👨‍💼
Sarath V
Senior Software Engineer · 12 yrs
Short outcome badge
"I was invited to join the AI team, got a good salary hike, and moved to work in AI."
👨‍💻
Punyakeerthi BL
Senior Software Engineer · 9 yrs
Joined company AI team
"This program didn't just teach me AI - it helped me think and build like an AI engineer."
🧑‍💻
Sudarshan Vidhate
Frontend Engineer · 7 yrs
Promoted to AI Product Engineer

Frequently asked questions

Everything you need to know about the program and your transition into Agentic AI.

Yes. You will learn how to design and build Agentic AI systems during the mentorship program. Agentic AI refers to AI systems that can plan, reason, retrieve information, use external tools like APIs and databases, and complete multi-step workflows autonomously. In our program, you will learn how to architect these systems using modern LLM APIs, RAG pipelines, and tool-calling frameworks, then deploy them in real-world projects.
Mentees build production-ready AI systems tailored to their industry. Examples include AI interviewers, onboarding assistants, research agents, monitoring systems, workflow automators, and domain-specific AI copilots. Your mentor helps you design an agentic architecture that integrates APIs, databases, and business workflows relevant to your career goals.
Our curriculum is fully personalized. It is designed based on your technical background, career goals, and current skill level. We create a structured learning pathway tailored to your schedule and transition timeline, ensuring that you build relevant AI systems without disrupting your existing commitments.
No. This is a personalized mentorship program, not a classroom-style course. You will learn at your own pace using curated resources and apply knowledge through guided projects. Your mentor acts as your accountability partner, helping you solve real problems and build deployable systems.
You should plan to commit 10 to 15 hours per week. This includes self-learning, building projects, mentor discussions, and refining your AI systems. The program is flexible, but consistent effort is essential for transitioning successfully into Generative and Agentic AI roles.
Yes. Most of our mentees come from software engineering backgrounds with no prior ML experience. The AI career transition path we follow focuses on applied system design, LLM integration, and building real products. Your development experience is the foundation we build on.
This program helps you stay relevant in an AI-driven industry. Instead of competing with AI tools, you will learn how to design and deploy them. Professionals who can build and integrate Agentic AI systems will lead transformation within organizations, not be disrupted by it.
Yes. Many mentees implement AI solutions within their current organizations. We help you identify high-impact use cases in your domain and design AI agents that improve workflows, productivity, or decision-making, giving you immediate visibility and career leverage.
Rather than a certificate, you will build a portfolio of deployed Agentic AI products, published technical articles, and an optimized professional profile. These tangible outcomes carry more weight with employers and demonstrate real capability in Generative AI and Agentic AI.
Yes. Many companies sponsor their employees for upskilling in Generative AI. We provide the necessary documentation and program details to help you make the case to your employer. Corporate sponsorship is a common path for professionals joining the program.
The future belongs to builders. Become one.
Start your leap