Build the team you need with the one you have
Your AI engineering team can be performant and efficient at building, shipping, and sharing LLM prototypes to production.
you need help
Engineering team leaders are the linchpin between strategy and execution. But let's be honest, it's a mandate that requires you to do more with less. You need to maximize results without equally increasing hiring and operating costs. But how?
the struggle is real
We polled 400 engineering team leaders from our LinkedIn network and asked:
"What is the biggest pain point when it comes to building a proper AI Engineering team - one that regularly ships prototypes to production - that generates positive ROI?"
What is holding your team back?
💼 Talent & Skills
“We can’t afford super-specialists; we need both AI and traditional SWE skills.”
🏗️ Prototyping Pipeline
“Takes too long to cycle through experiments.”
🧰 Tooling & Infrastructure
“Many tools out there… we want max results with lowest compute costs.”
🔏 Data, Compliance, and Risk
“Data sensitivity and lack of explainability, slow company adoption.”
“Compliance, risk, and audit add complexity from POC to deployment.”
Moving from prototyping to production often requires a combination of on-prem and cloud tools to maximize development velocity.
Build A Plan For Your TeamWhy choose The AI Engineer Bootcamp
We combine cutting-edge curriculum, expert instructors, dedicated peer supporters, and a supportive community to deliver an unmatched learning experience.
Build, Build, Build!
17+ builds per cohort!
Live Demo Day
Present to a live audience!
Dedicated Peer Support
8:1 Student-to-Peer Ratio
Accountability
Get expert feedback on all your work!
Certification
Become a Certified AI Engineer
Expert Instructors
Real AI Engineering Practitioners
Global Community
Work with people around the world
Enterprise Edge
Build On-Prem and in the cloud
Students Taught
Certified Graduates
Free Videos
Discord Members
I just finished the AI Engineering Bootcamp from AI Makerspace and I can't recommend it enough! 🚀🚀 This program is a game-changer for anyone looking to build practical, deployable AI skills. I'm already applying what I learned to my projects and at work.
Nishanth Nair
Director of Engineering
Visa
Course Curriculum
Stay at the bleeding-edge of LLMs, RAG, MCP, A2G and dozens of other concepts, code, and best practices with the most comprehensive curriculum, anywhere!
Industry-leading tooling, LLMs, frameworks & infrastructure
Week 1
Intro, Vibe Check & RAG
Session 1
- Meet your cohort, peer supporters, and journey groups!
- LLM prototyping best practices, from scoping to prompting to vibe checking
- How to succeed as a certified AI Engineer on Demo Day!
Session 2
- Build a Python RAG app from scratch!
- Overview of Prompt Engineering best-practices and the LLM App Stack
- Understand embedding models and similarity search
- Understand RAG = Dense Vector Retrieval + In-Context Learning
Week 2
Production rag apps
Session 3
- Understand the state of production LLM application use cases in industry
- Understand Demo Day expectations
- Ideate with peers & peer supporters
- Build an end-to-end RAG application using everything we’ve learned so far!
Session 4
- Why LangChain, OpenAI, QDrant, LangSmith?
- Understand LangGraph & LangChain core constructs
- How to use LangSmith as an eval & monitoring tool for RAG apps!
- Build a RAG system with LangChain and Qdrant!
Week 3
Agents & Multi-Agent Systems
Session 5
- Answer the question: “What is an agent?”
- Understand how to build production-grade agent applications using LangGraph
- How to use LangSmith to evaluate more complex agentic RAG applications!
Session 6
- Production-Grade agents with LangGraph
- Understand what multi-agent systems are and how they operate
- Build a production-grade multi-agent applications using LangGraph
Week 4
RAG & Agent Eval with SGD
Session 7
- An overview of assessment and Synthetic Data Generation for evaluation
- How to use SDG to generate high-quality testing data for your RAG application
- How to use LangSmith to baseline performance, make improvements, and then compare
Session 8
- Build RAG and Agent applications with LangGraph
- Evaluate RAG and Agent applications quantitatively with the RAG ASsessment (RAGAS) framework
- Use metrics-driven development to improve agentic applications, measurably, with RAGAS
Week 5
Advanced RAG & Agentic Reasoning
Session 9
- Understand how advanced retrieval and chunking techniques can enhance RAG
- Compare the performance of retrieval algorithms for RAG
- Understand the fine lines between chunking, retrieval, and ranking
- Learn best practices for retrieval pipelines
Session 10
- Discuss best-practice use of reasoning models
- Understand planning and reflection agents
- Build an Open-Source Deep Research agent application using LangGraph
- Investigate evaluating complex agent applications with the latest tools
Week 6
Cert Challenge & Open AI SDK
Session 11
- Introduce the Certification Challenge!
- Pitch your problem, audience, and solution
- Network with people outside of your group!
Session 12
- Understand the suite of tools for building agents with OpenAI
- Core constructs of the Agents SDK and comparison to other agent frameworks
- How to use monitoring and observability tools on the OpenAI platform
Week 7
Contextual Retrieval & Code Agents
Session 13
- Understand the suite of tools for building agents with OpenAI and the evolution of their tooling
- Core constructs of the Agents SDK and comparison to other agent frameworks
- How to use monitoring and observability tools on the OpenAI platform
Session 14
- Defining code agents, coding agents, and computer use agents
- Understand the suite of tools for building code agents with Hugging Face’s Smol Agents library
- Understand the landscape of coding and computer use agents
Week 8
Prod. Endpoints & LLM Ops
Session 15
- Important production-ready capabilities of LangChain under the hood
- Deploy open LLMs and embeddings to scalable endpoints
- How to choose inference server
- Build an enterprise RAG application with LCEL
Session 16
- Defining LLM Operations (LLM Ops)
- Monitor, visualize, debug, and interact with LLM apps
- Deploy your apps to APIs directly via LangGraph Platform
- Build an enterprise RAG application with LCEL
Week 9
On-Prem Agents
Session 17
- Introduction to Building On-Prem
- Hardware & compute Considerations
- Local LLM & Embedding Model Hosting Comparison
- Build and present an On-Prem Solution to stakeholders
Session 18
- How to use Prompt caching
- Build/iterate on version-controlled Prompt and Tool Libraries
- Model Context Protocol (MCP) and Agent2Agent (A2A) Protocols
- MCP servers: inside vs. outside the enterprise
Week 10
Demo Day Week!
Session 19
- Code freeze
- Full Dress Rehearsal
Session 20
- Present live to the public
- Recorded for your resume and LinkedIn
- Invite your co-workers, family, and friends!
- Graduation!
The reactions from the team were overwhelmingly positive. They learned a lot and liked the way your Bootcamp is structured. Most importantly, they felt pride in completing the challenges... There was blood, sweat, and tears, but a huge sense of accomplishment! I will explore opportunities to work together in the future, so please keep me in the loop on what you're building over there.
Robert Fitos
AI Tribe Lead
Hrvatski Telekom
learn right now
Check out our live event, RAG: The 2025 Best-Practice Stack where we dive deeper into moving from prototype into production and give you a step-by-step look into what it takes to move your LangGraph-powered RAG prototype from a laptop demo, to a scalable, cloud-ready product.
Discover New Best Practices