AI Builder Program
Engineer real AI products. Inside a startup.
Is this you?
Build real-world AI products and solve industry challenges inside a startup environment.
Portfolio + internship experience + startup exposure.
What you'll learn & build.
A module-by-module breakdown. Every module mixes concepts, hands-on labs and a mini-deliverable you can show off.
Engineering Foundations
Set up like a real software engineer.
- Python deep-dive: typing, OOP, async, packaging
- Git, GitHub, branching & code reviews
- Linux, shell, virtual environments & IDEs
- Working with APIs, JSON, .env and secrets
Math for Machine Learning
Build the intuition behind the algorithms.
- Statistics, probability & hypothesis testing
- Linear algebra: vectors, matrices, decompositions
- Calculus & gradients for optimization
- Information theory & loss functions
Data Engineering Essentials
Move from notebooks to real data pipelines.
- Pandas at scale, NumPy & data structures
- SQL for analytics & window functions
- MongoDB & vector databases
- PySpark & Airflow basics
Applied Machine Learning
Build, tune and ship real ML models.
- Regression, classification, clustering
- Ensembles: Random Forest, XGBoost, LightGBM, CatBoost
- Feature engineering, scaling, encoding
- Cross-validation, hyperparameter tuning & evaluation
Deep Learning
Neural networks for real-world problems.
- Neural networks, backprop & optimization
- CNNs for computer vision (classification, detection)
- RNN / LSTM / Transformers for sequences
- Transfer learning with PyTorch & TensorFlow
Natural Language Processing
Make machines understand text.
- Text preprocessing with NLTK & spaCy
- Embeddings & vectorization
- Sentiment, classification, NER, summarization
- Hugging Face Transformers in practice
Generative AI, LLMs & RAG
Build the systems behind modern AI products.
- How LLMs work: tokens, embeddings, attention
- Prompt engineering & structured outputs
- Retrieval-Augmented Generation (RAG) end-to-end
- Vector databases: Pinecone, Chroma, pgvector
- LangChain & LlamaIndex pipelines
AI Agents & Tool Use
Go beyond chat — build agents that act.
- Agent architectures & function calling
- Tool use, planning & multi-step workflows
- Multi-agent systems & orchestration
- Evaluation, guardrails & safety
APIs, Deployment & MLOps Basics
Ship models behind real APIs.
- Building APIs with FastAPI / Flask
- Docker, environments & reproducible builds
- CI/CD basics for ML projects
- Cloud deployment (AWS / GCP / Azure essentials)
- Monitoring, logging & model drift
Industry Projects Inside the Startup
This is where it gets real.
- Team-based product builds on real datasets
- Code reviews, sprints & shipping cycles
- Working with PMs, designers & stakeholders
- Production-grade documentation
Career & Internship Launch
Walk out with proof of work.
- Portfolio of shipped products on GitHub
- Internship deliverables & recommendation letter
- Technical interview prep & system design intro
- LinkedIn, resume & founder/recruiter intros
Builder focuses on these levels
Every track moves through the same four-level ladder. Your track emphasizes the highlighted levels.
Understand the language, tools and thinking of AI.
Ship real projects with modern AI stacks.
Take real industry problems and craft AI solutions.
Drive teams, products and strategy with AI.
Outcomes.
Ready to build your future in AI?
Stop collecting certificates. Start building experience.