What You'll Learn, Module by Module.

The full-stack toolkit the rest of the program runs on: Python backend development with FastAPI, Git and GitHub workflows, PostgreSQL and MongoDB, Docker, CI/CD pipelines, authentication, prompt engineering, and cloud fundamentals — plus the modern AI dev toolchain: Cursor, Claude Code, OpenAI Codex, and Devin.

Work with real commodity price and market data on a live intelligence platform — build ETL pipelines orchestrated with data pipelines, time-series forecasting models, BI dashboards, and interactive charts backed by production SQL. Introduction to scalable data pipeline architecture.

Build real-time buyer-seller messaging and location-aware discovery on a live marketplace product, using WebSockets, event-driven patterns, streaming data pipelines, Python, and REST APIs. Understand how production systems handle millions of real-time events.

Use generative AI to automate multi-step research workflows on a live AI product — prompt engineering as an engineering discipline, multi-modal AI applications, OpenAI and Claude SDKs at depth, and retrieval and synthesis systems that turn raw data into sourced, structured answers.

Build production AI agents using the full modern stack: LangChain and LangGraph for orchestration, LlamaIndex for retrieval-augmented agents, CrewAI and AutoGen for multi-agent collaboration, PydanticAI for type-safe agent building, and OpenAI Agents SDK and Claude Agent SDK for provider-native agents — with tool calling, agent memory, MCP, and A2A communication.

Build the retrieval layer that makes AI systems trustworthy: vector databases including Pinecone, ChromaDB, Weaviate, Qdrant, and FAISS; production RAG pipelines with embedding strategies; and AI quality measurement with RAGAS, TruLens, and DeepEval — plus LangSmith observability and text-to-SQL and text-to-dashboard capstones.

Own the growth, data, and product layer of live web products: analytics, SEO and AEO, ad platform integrations, data scraping pipelines, React and Next.js, Vercel deployments, Docker-containerized FastAPI services with CI/CD, microservices, AWS cloud deployment, and an Agile feature-development lifecycle end to end.

Ship an end-to-end AI data product integrating agents, RAG, and production deployment, present at Demo Day to founders and hiring partners, and enter screening for paid internship roles at Trida Labs — backed by mock interviews and resume overhaul.