From Zero to AI Engineer
in 10 Weeks
A cohort-based, deployment-focused program for developers and analysts who want to build real AI systems — not just get a certificate. Covering RAG, LLM agents, automation, and production deployment.
Cohort format with weekly live sessions
Every module has a coding exercise or mini-project
Join from anywhere in India
Graduate with a real deployed AI system
9 Modules + a Capstone Project
Each module builds on the last. By week 10, you'll have the skills to design, build, and deploy a real-world AI system from scratch.
Python & ML Foundations
- Python for AI/data workflows
- Core machine learning concepts and intuition
- Working with APIs: REST, auth, rate limits
- Data manipulation with pandas and numpy
Solid Python and data fundamentals to hit the ground running with LLMs.
LLM Fundamentals
- How large language models work (transformers, attention)
- Tokens, context windows, and model families
- Model selection trade-offs: speed, cost, capability
- LLM APIs for developers: OpenAI, Anthropic, open-source
Understand what LLMs can and can't do — and how to reason about them as a developer.
Prompt Engineering
- Prompt design patterns: zero-shot, few-shot, chain-of-thought
- Structured outputs with function calling and JSON mode
- Prompt evaluation frameworks and automated testing
- Prompt tuning workflows and iteration strategies
Write prompts that consistently produce reliable, structured, testable outputs.
RAG Fundamentals
- Vector databases: concepts, indexing, and querying
- Text embeddings: what they are, how to choose them
- Dense vs. sparse retrieval strategies
- Building a basic document Q&A pipeline end-to-end
Build a working RAG system that answers questions from real documents.
Advanced RAG
- Hybrid retrieval: combining dense and sparse approaches
- Re-ranking models to improve precision
- Chunking strategies for different document types
- Handling real-world messy documents (PDFs, scans, tables)
Build production-quality RAG that handles real, imperfect documents reliably.
Multi-Agent Systems
- Agent architecture: tools, memory, planning
- Agent orchestration with LangGraph
- Tool use: web search, code execution, API calls
- Multi-step reasoning workflows and error handling
Design and implement multi-agent systems that can tackle complex, multi-step tasks.
Fine-Tuning vs. RAG
- When to fine-tune vs. when to retrieve
- Cost and latency trade-offs in real deployments
- Practical decision framework for choosing your approach
- Brief hands-on fine-tuning exercise with a small model
Make confident, defensible architecture decisions for any AI project.
Automation & Integration
- n8n workflow automation: nodes, triggers, and flows
- WhatsApp Business API integration
- Connecting AI outputs to business systems (CRMs, DBs, Sheets)
- Building a full chatbot pipeline end-to-end
Integrate AI into real business workflows — not just notebooks.
Production Deployment
- Serverless deployment patterns and containerisation
- Cost optimisation: caching, batching, model selection
- Observability: logging, tracing, and monitoring LLM calls
- Taking a prototype to production: checklist and gotchas
Ship real AI systems that stay up, stay cheap, and stay observable.
End-to-End AI System
Build and deploy a complete AI system for a real-world domain. Example: a document Q&A assistant for insurance/policy documents — ingestion pipeline, RAG retrieval, conversational interface, and production deployment.
Parse, chunk, embed, and index real documents
Hybrid search with re-ranking for precision
WhatsApp or web chat with multi-turn memory
Live, monitored, cost-optimised system
Ready to Become an
AI Engineer?
Cohorts are kept small to maintain quality. Contact us for the next cohort start date, pricing, and eligibility.
What You'll Graduate With
- ✓A deployed, real-world AI system (your capstone)
- ✓Ability to build RAG pipelines from scratch
- ✓Multi-agent orchestration with LangGraph
- ✓Automation workflows with n8n
- ✓Production deployment and monitoring skills
- ✓A community of fellow AI engineers