Back to InitiativesMarie Wilson Advanced Skill Development Center · R.K. Nagar

AI Engineer Bootcamp

A Twelve‑Week Journey from Foundations to Capstone

12 weeks · full-time intensity
Free for youth of Radhakrishnan Nagar
Certificate + Demo Day on completion
Enroll Now — Free
Full Syllabus

What you will learn, week by week

Every module builds on the last — from environment setup through advanced LLM pipelines to a production-grade capstone you can showcase on your portfolio.

01
Week 01

What is AI Engineering & Environment Setup

  • Understand the differences between an AI Engineer, ML Engineer, and Data Scientist, and review the career roadmap and salary landscape globally and in India.
  • Gain an overview of key tools including Python, FastAPI, LangChain, OpenAI, and HuggingFace, and explore how modern AI apps are built using end-to-end architecture.
  • Review real-world AI product demos such as ChatGPT, Copilot, and Perplexity.
  • Set up your development environment by installing Python 3.11+, pip, virtual environments (venv/conda), Docker Desktop, and VS Code with essential extensions (Python, Pylance, GitLens, REST Client).
  • Configure a Git and GitHub account with SSH keys, and learn bash terminal fundamentals including navigation, chmod, and environment variables.
  • Write your first "Hello AI World" Python script, create and push to a GitHub repository, and understand the structure of .gitignore, README.md, LICENSE, and requirements.txt files.
  • Review course expectations and grading, form teams, and access essential resources like Roadmap.sh, HuggingFace, and OpenAI documentation.
02
Week 02

Python Mastery for AI Engineers

  • Refresh core Python fundamentals, covering data types, comprehensions, unpacking, the walrus operator, and functions (args, kwargs, decorators, closures).
  • Implement exception handling patterns specifically used in APIs.
  • Work with JSON, CSV, and YAML files, use the requests library to call REST APIs, and apply regular expressions for text preprocessing.
  • Master Async Python by understanding asyncio, async/await, event loops, and aiohttp for async HTTP calls, and learn why async matters in AI for streaming responses.
  • Build a Python CLI tool that calls the OpenAI API and saves the output to a file.
03
Week 03

Data Structures, OOP & Clean Code

  • Learn Object-Oriented Programming (OOP) in Python, focusing on classes, inheritance, and dunder methods.
  • Understand data structuring using dataclasses and Pydantic models.
  • Study core data structures including lists, dicts, sets, queues, and stacks, alongside basic algorithms for sorting and searching.
  • Explore basic concepts such as recursion, generators, and itertools.
  • Apply clean code practices utilizing PEP8, type hints, and mypy, and learn how to write unit tests using pytest.
  • Participate in code review labs using a structured rubric and collaboratively refactor poorly written scripts.
04
Week 04

APIs & FastAPI Introduction

  • Understand core REST API concepts, including HTTP methods (GET, POST, PUT, DELETE, PATCH), status codes, headers, and the request/response lifecycle.
  • Differentiate between JSON, Form data, and Multipart formats, and test APIs using tools like Postman, curl, and the VS Code REST Client.
  • Dive into FastAPI project architecture, exploring routers, models, schemas, path parameters, query parameters, and request bodies using Pydantic.
  • Learn dependency injection, the appropriate use of async endpoints, and how to utilize autogenerated documentation via Swagger UI and ReDoc.
  • Build a Task Manager REST API with full CRUD operations, integrating validation, error handling, and documentation.
05
Week 05

GitHub, CI/CD & Developer Workflows

  • Master Git branching strategies (GitFlow, trunk-based development), understand merge versus rebase, and learn how to resolve merge conflicts.
  • Utilize advanced Git commands such as stash, cherry-pick, and bisect.
  • Collaborate effectively by writing and reviewing Pull Requests, and managing work via GitHub Issues, Projects, and Milestones.
  • Set up CI/CD pipelines using GitHub Actions and safely manage secrets.
06
Week 06

FastAPI Advanced + Database Integration

  • Containerize Python FastAPI applications using Dockerfiles, configure multi-service setups with docker-compose, and push images to Docker Hub or GitHub Container Registry.
  • Deploy FastAPI applications to cloud platforms (Railway or Render) and manage production configuration using environment variables.
  • Integrate databases using SQLAlchemy ORM (models, sessions, relationships) with PostgreSQL, and manage database migrations using Alembic.
  • Implement asynchronous database operations with asyncpg.
  • Apply FastAPI production patterns including JWT Authentication (OAuth2PasswordBearer), background tasks, WebSockets, rate limiting, CORS, and middleware.
  • Enhance performance by caching API responses with Redis, implementing connection pooling, optimizing queries, and profiling applications with py-spy.
  • Upgrade the Task Manager API project by adding authentication and database persistence.
07
Week 07

Machine Learning Fundamentals

  • Understand foundational ML concepts, differentiating between supervised, unsupervised, and reinforcement learning.
  • Learn crucial training principles such as train/test/validation splits, overfitting, underfitting, loss functions, gradient descent, and learning rate.
  • Apply Scikit-learn for practical ML, working with linear and logistic regression, decision trees, and random forests.
  • Perform feature engineering, normalization, and pipeline creation, and evaluate models using precision, recall, F1, and ROC-AUC.
  • Serve machine learning models by saving/loading with joblib or pickle, wrapping them in FastAPI endpoints, and managing model versions.
  • Train and serve a sentiment classifier via a custom API.
08
Week 08

Deep Learning & Neural Networks

  • Explore neural network foundations, covering perceptrons, activation functions, backpropagation, and conceptual overviews of CNNs (image) and RNNs (text).
  • Work with PyTorch basics, including tensors, autograd, and building training loops.
  • Dive into Transformer architectures by understanding the math and intuition behind attention mechanisms, and compare models like BERT, GPT, and T5.
  • Study tokenization strategies including BPE, WordPiece, and SentencePiece.
  • Implement embeddings and semantic search using Word2Vec, GloVe, Sentence Transformers, cosine similarity, and dot products.
  • Use the HuggingFace Transformers pipeline API to build a semantic search system on a custom dataset.
09
Week 09

LLMs & Prompt Engineering

  • Learn how state-of-the-art LLMs (GPT-4, Claude, Gemini, Llama) work, and understand parameters like context window, temperature, top-p, and max_tokens.
  • Gain hands-on experience using the OpenAI API, Anthropic API, and Groq API.
  • Master prompt engineering techniques, including zero-shot, few-shot, and chain-of-thought prompting, alongside system prompts, role prompting, and guardrails.
  • Force structured output from LLMs using JSON mode and function calling.
  • Understand when to fine-tune versus when to prompt engineer, and explore Parameter-Efficient Fine-Tuning (PEFT) methods like LoRA and QLoRA, along with a demo of the HuggingFace Trainer API.
  • Build a domain-specific chatbot that delivers structured output using the OpenAI API and FastAPI.
10
Week 10

RAG & Vector Databases

  • Understand Retrieval-Augmented Generation (RAG) architecture and why it outperforms fine-tuning for specific knowledge tasks.
  • Embed documents at scale and implement chunking strategies (fixed, semantic, recursive).
  • Compare vector databases like Pinecone, ChromaDB, Weaviate, and pgvector, and understand HNSW indexes and approximate nearest neighbour search.
  • Apply metadata filtering to optimize vector search results.
  • Build systems using LangChain (chains, prompts, memory, tools) and LlamaIndex (document ingestion, query engines), and implement hybrid search utilizing BM25 and vector data.
  • Develop a Q&A bot that queries custom PDF documents using RAG and FastAPI.
11
Week 11

Tools & Production Deployment

  • Ensure observability and evaluation using LangSmith, Weights & Biases, and Langfuse for tracing, and evaluate outputs using RAGAS and LLM-as-judge.
  • Deploy AI applications to production environments (AWS / GCP / Azure overview), utilize GPU serverless platforms (Modal, Replicate), and configure monitoring, cost tracking, and rate limiting.
  • Finalize teams and conduct architecture design sessions for the upcoming Capstone project.
12
Week 12

Capstone Project

  • Participate in a final build sprint to complete and polish a full-stack AI application.
  • Conduct code reviews with mentors to ensure performance and security, and deploy the project to production via a CI/CD pipeline.
  • Create comprehensive project documentation including a README, API docs, and a demo video.
  • Present the finished project on Demo Day (pitch and live demo) to guest evaluators and participate in peer voting.
  • Prepare for career advancement by setting up portfolios (GitHub, LinkedIn, personal site), optimizing resumes for AI Engineer roles, and learning how to contribute to open source.

Ready to become an AI Engineer?

Free enrollment for youth of Radhakrishnan Nagar. Seats are limited — register today and secure your place in the next cohort.

Fill Enrollment Form