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Program map

01 PROMPT~/capstone› build a support agent› with RAG + evals▍↵ run02 CONTEXT→chunks → embeddings → pgvector03 AGENT LOOPplanactobserveLLMclaudegptgeminisearchsqlmcptools · memory · human-in-the-loop04 EVALS0.92faithfulness50 cases · llm-judge0.8505 SHIPtypecheck · testsevals ≥ 0.85liveyourapp.dev

18weeks5phases

  1. 0P1–P2Pre-courseZero to coder
  2. 11–4FoundationsBasic
  3. 25–8Building AI productsIntermediate
  4. 39–12Agents and productionAdvanced
  5. 413–16Mastery and launchMaster
Waitlist openLive program

AI Engineer Program

In 18 weeks — a 2-week pre-course plus a 16-week core — a student goes from zero coding to shipping production AI products, and leaves with either a launched product or a job-ready portfolio and active job search.

Who it's for: anyone who wants to become an AI Engineer. Absolute beginners start at the pre-course; students who already code can skip it after a short entry test.

  • 18 weeksincl. 2-week pre-course
  • 5 phasesZero to coder → Master
  • Mon–ThuLive class (2–2.5 hrs)
  • FriReading day
  • SatAssignment day

Internship included

We provide an internship as part of the program. You work directly on our products, alongside the team that builds them.

Outcomes by Week 16

  • Builds LLM apps end to end: prompting, structured output, tool use, RAG, agents, MCP, evals, fine-tuning, deployment
  • Understands how LLMs work inside: tokens, attention, training, inference, quantization
  • Uses AI coding tools (Claude Code, Cursor, Codex, Copilot) to ship much faster without losing code quality
  • Has 4 portfolio projects plus 1 capstone live on a public URL
  • Founder track: a launched MVP with real users and a go-to-market plan
  • Career track: resume, GitHub, LinkedIn and X profiles ready, applications going out, mock interviews done

₹49waitlist

See curriculumSee brochure

Curriculum · 18 weeks · 5 phases

Week by week, day by day

See brochure

Weekly rhythm

Mon–ThuLive class (2–2.5 hrs)
You teach the week's 4 topics: concept, live build, Q&A
FriReading day
Students read the week's core articles + 2–3 latest articles on that week's topics, then post a 200-word summary
SatAssignment day
Students build the week's assignment and submit a GitHub link + short demo video
SunOff
Rest; optional doubt-clearing community call

AI usage rules by phase

P1–P2, Week 1
No AI code generation. AI only for concepts and explaining errors, with learning mode on
Weeks 2–8
Generate-then-explain: every AI-written block is explained in the PR or demo video
Weeks 9–16
Full agentic tools, plus one no-AI debugging drill each week

How students learn here

Students build more than they watch, and use AI in a way that keeps their own skills growing.

Monday recall quiz (10 min)
questions from last week and 3 weeks back, no notes
I do / We do / You do
in each live class you demo, the class builds one step together, then students write the next step alone before you reveal it
Active Friday
summary + one small experiment applying an article to their own project
Saturday debugging task
a broken repo fixed without AI, alongside the main assignment
Course reader
a written notes page per week that students keep

Cohort setup

  • Pods of 4–5 with an assigned buddy; mentor groups of 25 or fewer
  • Every student ships something visible in their first week
  • Project 3 and the capstone are unguided: a goal and a rubric, no step-by-step instructions
Phase 0Zero to coder

Pre-course

Weeks P1–P2

By the end of Week P2, a complete beginner can write Python, use Git and the terminal, call an API, and store data in Postgres — enough to start Week 1.

  1. Day 1 · Monday · Live class (2–2.5 hrs)

    How software works and setting up

      • CPU, RAM, storage, operating system
      • Files, folders, processes, what "installing" means
      • VS Code (and Cursor), Python 3, Node.js, Git
      • Package managers: Homebrew (Mac), winget (Windows), apt (Linux); WSL for Windows users
      • Navigation: pwd, ls, cd; absolute vs relative paths
      • Files: mkdir, touch, cp, mv, rm, cat, less
      • Environment variables and PATH; running scripts
      • Asking clear questions, pasting full error messages
      • Verifying answers instead of copying blindly
  2. Day 1 · Monday · Live class (2–2.5 hrs)

    Intermediate Python

      • imports, os, pathlib, json, csv, datetime, collections, random
      • reading and writing text, CSV, and JSON
      • try/except/else/finally, raising, custom exceptions
      • Classes, __init__, methods, inheritance
      • Dunder methods, @property, dataclasses
      • venv, pip, uv, requirements and pyproject.toml
Phase 1Basic

Foundations

Weeks 1–4

By Week 4, every student ships a deployed AI chatbot, codes daily with AI agents, and can explain how an LLM is trained and how it produces each token.

  1. Day 1 · Monday · Live class (2–2.5 hrs)

    The role and professional Python

      • AI Engineer vs ML Engineer vs Data Scientist vs Software Engineer
      • The AI stack: models, inference, orchestration, data and retrieval, evals, application
      • Tour of the 16 weeks and the two end tracks
      • uv, pyproject.toml, ruff (lint + format), mypy/pyright, pre-commit hooks
      • pytest, fixtures, parametrize, mocking external APIs
      • folder structure, logging, config with .env and pydantic-settings, secrets management
  2. Day 1 · Monday · Live class (2–2.5 hrs)

    The AI tools landscape

      • IDE assistants: Cursor, Windsurf, GitHub Copilot
      • Agentic/terminal coding: Claude Code, OpenAI Codex, Gemini CLI, Aider
      • App builders: v0, Lovable, Bolt, Replit Agent
      • Chat assistants for research and design: Claude, ChatGPT, Gemini
      • gathering context, tool calls, the edit-run-test loop, context limits
      • by task, cost, and team setup
      • what code leaves your machine, enterprise settings
  3. Day 1 · Monday · Live class (2–2.5 hrs)

    ML and deep learning foundations

      • vectors, matrices, dot product, probability, distributions, softmax, logarithms
      • supervised vs unsupervised, features and labels, train/validation/test split, overfitting, regularisation
      • accuracy, precision, recall, F1, confusion matrix
      • linear and logistic regression, decision trees, k-means
      • neurons, layers, activations, loss functions, gradient descent, backpropagation
      • tensors, autograd, a tiny training loop on GPU (Colab)
  4. Day 1 · Monday · Live class (2–2.5 hrs)

    Prompting fundamentals

      • system prompt, user and assistant turns, prefill
      • role, task, context, constraints, output format, success criteria
      • zero-shot, few-shot, choosing good examples
      • XML tags, delimiters, putting long documents first
      • Anthropic, OpenAI, and Google prompting differences
Phase 2Intermediate

Building AI products

Weeks 5–8

By Week 8, students have shipped a RAG app with measured quality and a full-stack AI SaaS with auth, payments, and cost control.

  1. Day 1 · Monday · Live class (2–2.5 hrs)

    Structured outputs

      • LLM output as data, not text
      • JSON mode vs strict structured outputs, schemas with Pydantic and Zod
      • Instructor, Outlines (constrained decoding), BAML
      • validation, retry with error feedback, fallbacks
      • partial objects for fast UIs
  2. Day 1 · Monday · Live class (2–2.5 hrs)

    Embeddings

      • meaning as vectors, similarity metrics (cosine, dot, Euclidean)
      • OpenAI, Voyage, Cohere, BGE, E5, Jina, sentence-transformers; multilingual models for Indian languages
      • dimensions, Matryoshka embeddings, normalisation, cost and speed
      • semantic search, clustering, deduplication, recommendations
      • UMAP/t-SNE plots of embeddings
  3. Day 1 · Monday · Live class (2–2.5 hrs)

    Evals foundations

      • vibes vs measurement
      • reading traces, open coding, grouping failures into categories
      • golden sets, edge cases, synthetic data generation
      • write the eval, then improve the system
  4. Day 1 · Monday · Live class (2–2.5 hrs)

    Product architecture

      • Clerk, Auth.js, Supabase Auth; roles; multi-tenancy
      • Postgres/Supabase/Neon, Drizzle or Prisma, row-level security
      • queues and background jobs (Celery, BullMQ, Inngest, Trigger.dev)
      • S3/Cloudflare R2, signed URLs, upload limits
      • webhooks, rate limiting, API design for your own product
Phase 3Advanced

Agents and production

Weeks 9–12

By Week 12, students build agents with LangGraph and MCP, add voice and vision, fine-tune a model, and run it all safely and cheaply in production.

  1. Day 1 · Monday · Live class (2–2.5 hrs)

    Agent foundations

      • an LLM using tools in a loop toward a goal
      • prompt chaining, routing, parallelisation, orchestrator-workers, evaluator-optimiser
      • ReAct, plan-and-execute, reflection, tree-of-thought (overview)
      • from copilot to fully autonomous; when NOT to build an agent
  2. Day 1 · Monday · Live class (2–2.5 hrs)

    Model Context Protocol (MCP)

      • the N×M integration problem
      • hosts, clients, servers
      • tools, resources, prompts, sampling, elicitation, roots
      • stdio vs Streamable HTTP
      • in Claude Desktop, Claude Code, Cursor; the MCP ecosystem and registries
  3. Day 1 · Monday · Live class (2–2.5 hrs)

    Vision and documents

      • image inputs, sending PDFs directly, screenshots and charts
      • when each wins on Indian documents (Aadhaar-style forms, invoices, handwriting)
      • frame sampling, Gemini video input
      • CLIP, SigLIP; multimodal RAG
  4. Day 1 · Monday · Live class (2–2.5 hrs)

    Observability

      • OpenTelemetry and GenAI semantic conventions; Langfuse, LangSmith, Helicone, Arize
      • time to first token, p50/p95 latency, tokens, cost, error and refusal rates
    1. 03Dashboards and alerts
      • sampling live traffic, drift detection, user feedback loops
Phase 4Master

Mastery and launch

Weeks 13–16

Every student builds a capstone, learns both how to launch a product and how to get hired, then pushes hardest on their chosen track in Week 16.

  1. Day 1 · Monday · Live class (2–2.5 hrs)

    Finding a real problem

      • your own pain, workflows full of documents, calls, or repetitive decisions
      • customer interviews (The Mom Test), signs of real demand, pre-sales and waitlists
      • proprietary data, workflow depth, distribution, evals
      • India vs global, B2B vs B2C, who pays
  2. Day 1 · Monday · Live class (2–2.5 hrs)

    Positioning and pricing

      • one-line pitch, ideal customer, competitor map
      • headline, demo video, social proof, call to action
      • usage-based vs seats vs outcome-based, free tiers, margins after model costs
      • purchasing power, UPI and international payments
  3. Day 1 · Monday · Live class (2–2.5 hrs)

    The AI job market and resume

      • AI engineer, applied AI engineer, LLM backend engineer, forward-deployed engineer, AI product engineer
      • shipped projects, evals, production thinking, communication
      • one page, project-first, numbers and outcomes, ATS-friendly
      • mapping skills to your projects
  4. Day 1 · Monday · Live class (2–2.5 hrs)

    Capstone polish

    1. 01Evals, observability, and a security pass
    2. 02README, architecture diagram, and 3-minute demo video

Capstone requirements

  • Solves a real problem for a named user group
  • Uses at least 2 of: RAG, tool use, agents (LangGraph or similar), MCP, multimodal/voice, fine-tuning
  • Has an eval suite, tracing, guardrails, and a cost estimate per user
  • Deployed on a public URL with auth
  • README, 3-minute demo video, and a published case study

Portfolio at graduation

  1. 01Domain chatbotPrompting, streaming, deploymentWeek 4
  2. 02RAG app with evalsRetrieval, evals, DSPyWeek 7
  3. 03AI SaaSFull-stack, auth, payments, cost controlWeek 8
  4. 04MCP server + agentAgents, MCP, tool useWeek 10
  5. 05CapstoneEverything, production-readyWeek 16

Grades come from shipped work, not tests: 60% weekly assignments, 30% capstone, 10% reading summaries and participation.

Complete AI Engineer skills map28 areas
Dev fundamentalsWeek P1–P2
Terminal, Git/GitHub, Python, JS/TS, React, HTTP, SQL, Postgres
Engineering toolkitWeek 1
uv, ruff, pytest, Pydantic, asyncio, tenacity, NumPy, pandas, SQLAlchemy, FastAPI, Docker, Redis
AI coding toolsWeek 2
Claude Code, Cursor, Windsurf, Copilot, Codex, Gemini CLI, Aider, v0, Lovable, Bolt, CLAUDE.md/AGENTS.md, worktrees
Full-stackWeek 2, 4, 8
Next.js App Router, Tailwind, shadcn/ui, Drizzle/Prisma, Vercel AI SDK
ML foundationsWeek 3
Vectors, softmax, train/test, metrics, scikit-learn, neural nets, backprop, PyTorch
LLM internalsWeek 3
BPE tokenization, embeddings, RoPE, attention, decoder-only, pre-training, SFT, RLHF, DPO, reasoning models, sampling, KV cache, MoE
Models and APIsWeek 3
Claude, GPT, Gemini, Llama, Qwen, DeepSeek, Sarvam; SDKs, OpenRouter, LiteLLM, Bedrock, Vertex, Batch APIs
Open models and inferenceWeek 3
Hugging Face, Ollama, llama.cpp, quantization (GGUF/AWQ/GPTQ), vLLM, SGLang, TGI, Groq, Modal, RunPod
Prompt and context engineeringWeek 4
System prompts, few-shot, XML, chain-of-thought, chaining, prompt caching, compaction, promptfoo, memory (Mem0, Zep, Letta)
Structured output and toolsWeek 5
JSON schema, Instructor, Outlines, BAML, function calling, tool design, text-to-SQL
LLM frameworksWeek 5
LangChain, LlamaIndex, Pydantic AI, Haystack
Embeddings and vector DBsWeek 6
OpenAI/Voyage/Cohere/BGE embeddings, pgvector, Pinecone, Qdrant, Weaviate, Chroma, Milvus, FAISS, HNSW
RAGWeek 6
Parsing (Docling, Unstructured, LlamaParse), crawling (Firecrawl), chunking, BM25, hybrid search, reranking, citations
Advanced RAGWeek 7
HyDE, query rewriting, contextual retrieval, ColBERT, ColPali, GraphRAG, Neo4j, agentic RAG
EvalsWeek 7
Error analysis, golden sets, LLM-as-judge, RAGAS, DeepEval, Braintrust, LangSmith, Langfuse, Phoenix
OptimisationWeek 7
DSPy (MIPROv2, GEPA), semantic caching
SaaS engineeringWeek 8
Auth (Clerk, Supabase), queues (Celery, Inngest), S3/R2, Razorpay, Stripe, metering, generative UI
AgentsWeek 9
ReAct, plan-and-execute, reflection, agent loop from scratch, budgets, memory
Agent frameworksWeek 9
LangGraph (state, checkpointers, interrupts, subgraphs, Studio), OpenAI Agents SDK, Claude Agent SDK, CrewAI, AutoGen, Google ADK, smolagents, Mastra
MCP and multi-agentWeek 10
MCP primitives and transports, FastMCP, OAuth, A2A, supervisor/swarm, subagents
Autonomous agentsWeek 10
Browser Use, Playwright, Stagehand, computer use, E2B sandboxes, Temporal, trajectory evals
Multimodal and voiceWeek 11
VLMs, OCR, CLIP, Whisper, Deepgram, ElevenLabs, Realtime APIs, LiveKit, Pipecat, Bhashini
Fine-tuningWeek 11
LoRA, QLoRA, SFT, DPO, GRPO, distillation, TRL, PEFT, Unsloth, Axolotl
Generative mediaWeek 11
Diffusion, Flux, Stable Diffusion, ComfyUI, Veo, Runway
Production and LLMOpsWeek 12
OpenTelemetry, Helicone, guardrails, Llama Guard, Presidio, red teaming, gateways, load testing, Kubernetes, GitHub Actions, Terraform
Responsible AI and lawWeek 12
OWASP LLM Top 10, DPDP Act, EU AI Act, bias, model licences
Product and founder skillsWeek 13–14
Validation, MVP scoping, AI system design, pricing, launch, SEO/GEO, analytics, funding
Career skillsWeek 15
Resume, portfolio, interviews, job platforms, negotiation