AI bootcamp 2

Most AI bootcamps focus on either beginner programming with AI APIs or narrow workflows like basic RAG or fine-tuning. Bootcamp 2 is designed for engineers who want to master advanced AI systems engineering — including byte-level modeling, multi-vector and multimodal RAG, reinforcement learning (DPO, PPO, RLVR), verifier-guided pipelines, and tool-using agents. It combines deep technical theory with 50+ hands-on labs to build production-ready systems. To my knowledge, few programs cover advanced RAG and RLHF together at this depth, especially with a focus on evaluation, feedback loops, and enterprise deployment.

Bootcamp 2 is a project-focused program where you learn by building advanced AI pipelines from scratch. It’s built for people who want to go beyond prompt engineering and API wrappers, and gain mastery in byte-level models, reasoning simulation, advanced retrieval architectures, and RL-based fine-tuning. You’ll work on multimodal RAG, feedback-driven rerankers, and RLHF-ready agents — with direct guidance from expert instructors who have shipped large-scale AI products.

This bootcamp is ideal for engineers, data scientists, and technical founders who want to build enterprise-ready AI systems. Whether you’re implementing advanced RAG for private, on-premise data, fine-tuning models with smaller datasets, or applying RLHF techniques to AI agents, this program equips you with the skills and frameworks needed to operate at a senior level.

You must have basic Python programming and debugging skills. This is not a beginner’s AI course — it assumes you can follow code, run experiments, and troubleshoot errors. Prior completion of Bootcamp 1 or equivalent experience is recommended.

Weekly commitment is approximately 3 hours for lectures and office hours, plus 2–4 hours of hands-on coding. The program runs for multiple weeks, with 50+ labs and mini-projects, culminating in enterprise-ready systems you can deploy. All sessions are recorded, and live schedules are designed to accommodate different time zones.

You need to be comfortable programming in Python and committed to completing advanced coding projects. We assume you have completed Bootcamp 1 or have equivalent knowledge of LLM fundamentals.

It’s best to come with a project idea — for example, a multimodal RAG system for your company data or an RLHF-tuned assistant for a niche use case. The curriculum is flexible enough to adapt your learning to your goals.

Bootcamp 2 is uniquely focused on advanced RAG, reinforcement learning, and RLHF. You’ll learn to design and debug full-stack AI systems with evaluation and feedback loops, not just call APIs. You also get mentorship from practitioners who have shipped large-scale AI systems and can help you adapt the techniques to your own projects.

This bootcamp goes far beyond generative AI basics. You’ll explore advanced model architectures, reasoning control, retrieval fusion across modalities, RL-based adaptation, and multi-hop agent planning. Everything is taught through runnable code that you can adapt to real-world applications.

Graduates are equipped for roles such as senior AI engineer, AI systems architect, enterprise AI consultant, and startup founder in the AI space. You will have the skills to build, deploy, and optimize advanced AI systems for enterprise-grade use cases.

Yes. You will complete over 50 labs and multiple large-scale projects, including a full multimodal RAG pipeline, feedback-driven reranker tuning, and RLHF-ready agent deployment. Every project is designed to be directly applicable to real-world scenarios.

Yes. If you commit to the work, we guarantee you will complete a project that meets your goals. We will align on project scope, budget, and time commitment upfront, and provide ongoing guidance to ensure you can ship a functional, well-evaluated system.

There are three core groups: engineers applying advanced RAG and RL fine-tuning for private data; builders creating vertical foundational models on smaller datasets; and technical professionals leveraging these skills for consulting or AI startup ventures.

Yes. You will build retrieval systems and agents that handle text, images, tables, audio, and structured data — integrating them into unified, query-routed pipelines.