Syllabus · 336 live hours

AI Engineer Course Syllabus — Every Module, No Form to Fill

Five phases across 6 months. Each one builds on the last, which is why we don't let you skip ahead. Here is exactly what gets taught, and why in this order.

  1. Months 1–2

    Python & software foundations

    Writing real software, not scripts: clean functions, classes, error handling, virtual environments and Git from week one. By the end you build and document REST APIs with FastAPI, the same way AI services are exposed in production.

    • Python from zero (OOP, files, generators)
    • Linux, Bash & Git
    • FastAPI & REST APIs
    • Pydantic & Postman
    • 500+ graded coding exercises
  2. Month 3

    Machine learning, deep learning & NLP

    Enough ML to reason about models: training vs evaluation, overfitting, metrics that mislead. Neural networks and NLP lead up to the transformer architecture that every modern LLM is built on.

    • Core ML algorithms & evaluation
    • Neural networks
    • NLP pipeline, Word2Vec
    • Transformers & attention
  3. Months 3–4

    Generative AI & LLMs

    Working with hosted and open models, prompting as a testable artefact, embeddings and vector search, then full retrieval-augmented generation over private documents, plus fine-tuning and evaluation.

    • Prompt engineering
    • OpenAI & Hugging Face models
    • Embeddings & vector databases
    • RAG over private documents
    • Fine-tuning & evaluation
  4. Months 4–5

    Agentic AI

    Tool calling, stateful graphs in LangGraph, agentic RAG, multi-agent collaboration and the Model Context Protocol, with the guardrails that keep agents reliable.

    • LangChain & LangGraph
    • ReAct & tool calling
    • Multi-agent systems
    • Agentic RAG
    • Model Context Protocol (MCP)
  5. Months 5–6

    MLOps, AWS & getting hired

    Containers, orchestration and CI/CD, then deployment on AWS services including Bedrock and SageMaker, with monitoring and cost control. In parallel: resume, LinkedIn, GitHub portfolio and weekly mock interviews.

    • Docker, Kubernetes, CI/CD
    • AWS: EC2, Lambda, S3, Bedrock, SageMaker
    • Monitoring & cost control
    • Resume, LinkedIn & GitHub portfolio
    • Weekly mock interviews

Deployed projects

  1. Production RAG chatbot over private company documents
  2. Multi-agent workflow built with LangGraph
  3. Fine-tuned domain-specific LLM
  4. NLP service serving live predictions through FastAPI
  5. Full MLOps deployment on AWS with CI/CD and monitoring

Practice & placement support

  • 1,000+ practice questions
  • 1,000+ interview questions
  • 500+ graded Python exercises
  • Guided prep for 2 AWS certifications
  • Resume, LinkedIn & GitHub portfolio reviews
  • Weekly mock interviews in the final phase

Total fee ₹34,994. See the fee schedule →

Frequently asked questions

How many hours is the AI Engineer course?

336 hours of live classes over 6 months, about 14 hours a week, plus your own practice time.

Is the syllabus updated for new AI tools?

The core sequence stays stable, while tools and examples are refreshed as the ecosystem changes, for example as new agent protocols and model versions appear.

Do I need maths for machine learning?

School-level maths is enough to start. The course explains the intuition behind the maths as you need it, rather than front-loading theory.

Want the week-by-week plan?

Message us on WhatsApp and we'll send the detailed weekly topic plan for the next batch.

Get course details on WhatsApp