Student outcomes
Proof you can show
Don't just list what you know. Show what you can build.
Capstone projects
Final capstone options
Each program ends with real capstone projects. Here are the final capstones, program by program.
Generative AI Engineering
Enterprise RAG Knowledge Assistant with Hybrid Search, Reranking & Evaluation
A production-grade RAG system over internal company documents featuring hybrid search (semantic + keyword), a reranking layer, automated source citation checking, and evaluation metrics using golden test datasets.
Autonomous Multi-Agent Business Operations Hub with n8n Integration
A multi-agent workflow (using a supervisor agent and specialized worker agents for research, drafting, and database updates) integrated with n8n to automate complex cross-platform tasks (e.g., monitoring emails, processing spreadsheets, and triggering human-approval webhooks).
MCP-Powered Local Data Analyst Agentic Workspace
An AI agent built using the Model Context Protocol (MCP) framework that securely connects an LLM client to local file resources, databases, and external tool servers with strict permission controls.
Applied AI & ML Engineering
Production-Grade LLM RAG (Retrieval-Augmented Generation) System with Guardrails & Observability
An enterprise-ready Question-Answering system over private documents using a Hugging Face open-source LLM, Vector Database (e.g., Chroma/Milvus), and monitored with Evidently AI or TruLens.
Scalable MLOps Pipeline with Airflow/Prefect, Feast Feature Store & Evidently AI Monitoring
An automated, scheduled retraining and monitoring pipeline for a tabular prediction model (e.g., house price or fraud detection) in a simulated production environment.
High-Performance Edge AI Inference Engine with ONNX & TensorRT (Quantized Deployment)
A high-throughput, low-latency microservice that takes a heavy computer vision or NLP model, optimizes it using ONNX Runtime and Post-Training Quantization (PTQ), and deploys it on an optimized cloud container.
Data Science
Enterprise Internal Knowledge QA Agent (RAG System with LLMs)
Support agents spend too much time searching through hundreds of internal company policy documents, HR handbooks, and product manuals to answer customer or employee inquiries.
Scalable Real-Time Clickstream & User Behavior Pipeline with PySpark
A high-traffic streaming platform needs to process gigabytes of raw user event logs daily, clean the data, and compute real-time user engagement metrics to feed a recommendation model.
End-to-End MLOps Pipeline with Automated Drift Detection & Retraining
A credit scoring model’s accuracy degrades over time due to shifting macroeconomic conditions (concept drift), requiring an automated system to detect degradation and trigger retraining.
Data Engineering
The Enterprise Cloud Lakehouse
Deploying a fully automated production pipeline stack
Python Full Stack
Real-Time Collaborative Document & Whiteboard Editor (like Google Docs/Miro Lite)
Real-time multi-user typing/drawing synchronization, room-based WebSocket connections, background PDF export via worker queues, and autosave functionality.
Multi-Tenant SaaS Analytics Dashboard with Automated Reports
Organization multi-tenancy, Stripe subscription billing integration, role-based access control (Admin, Manager, Viewer), and scheduled background email reports.
AI-Powered Content & Resume Parser ATS (Applicant Tracking System)
PDF/Docx resume upload and text extraction, vector search/keyword matching against job descriptions, background processing for heavy parsing, and REST API rate-limiting.
Python Programming
The AI-Powered Market Intelligence Tracker Capstone
Scraping technology or finance news articles using requests and BeautifulSoup
Career guidance
Make your profile easy to shortlist
Alongside the curriculum, we guide you on presenting your work so recruiters and hiring managers can evaluate it.
LinkedIn profile
A clear headline with your target role and stack, and your best projects featured.
ATS-friendly resume
Standard headings, plain formatting, and projects that lead with what shipped.
GitHub portfolio
Your 3–4 best repositories with a README, tests and a deployed link.
Open-source work
Start small with docs fixes and good first issues, and list merged pull requests.
Learners
What learners say
Priya SharmaJunior AI EngineerGenerative AI Engineering The Generative AI training helped me understand how LLMs and RAG-based applications work in practice. The hands-on learning strengthened my understanding of modern AI engineering and helped me prepare for opportunities in this growing field. I subsequently secured a role as a Junior AI Engineer at a product startup.
Ananya IyerData AnalystData Science The Data Science sessions helped me build confidence in Python, data analysis, and solving technical problems. The practical explanations helped me connect theoretical concepts with real-world applications. I'm excited to have secured my first role as a Data Analyst at a fintech company.
Rohan MehtaMachine Learning EngineerApplied AI & ML Engineering The Machine Learning training gave me a stronger foundation in model development, evaluation, and practical problem-solving. It helped me strengthen my technical skills and approach interviews with greater confidence. I was subsequently able to secure a new role as a Machine Learning Engineer, marking an important milestone in my career.
Karthik NairPython Full Stack Going from a React frontend to a deployed Python backend in one program was exactly what I needed.
Start building your proof
Pick a program and join the free demo on Saturday at 7:00 PM (IST).