Systems ship. Demos don't.
8 weeks. 48 hours live. You walk out with production-grade builds, an Enterprise Agentic RAG mid-program project, a capstone, and the architecture instincts that separate senior AI engineers from notebook coders. Join early — start with full recordings, get ahead before the live cohort begins.
✓ Live sessions Saturdays & Sundays · 8:00 PM – 11:00 PM WAT · Cohort 1 · Now Open
The gap between building a demo and owning production isn't skill — it's architecture thinking.
Jupyter notebooks that break at scale. Tutorials that skip error handling, memory management, and cost control. You can build a chatbot — but can you build one that serves 10,000 users without burning cash?
Latency spikes, runaway token costs, hallucination cascades in production. You pay for the outage, then pay again to refactor. Architecture decisions made early prevent expensive rewrites later.
Everyone claims "AI experience" on their resume. Recruiters can't tell who built real systems vs. who followed a YouTube tutorial. You need deployed, production-grade projects that speak for themselves.
Go from understanding individual components to orchestrating entire production AI systems.
Learn the building blocks of production AI
Master the core primitives — LLMs, agents, memory, and retrieval — through hands-on builds that progressively increase in complexity. By Week 4, you'll ship an enterprise-grade RAG system.
Orchestrate, deploy, and monitor at scale
Move from single agents to multi-agent systems, async pipelines, cloud deployment, and full observability. You'll graduate with a production-ready capstone you can demo anywhere.
Each week you learn the theory, then build something real. Click a week to see what you'll master.
Understand how LLMs actually work under the hood, then build a prompt optimizer that automatically selects the best prompting strategy for any given task.
Master LangChain's composability model and build an agent that can search the web, execute code, read files, and synthesize multi-source research reports.
Deep-dive into memory architectures and build an AI assistant that remembers user preferences, past conversations, and adapts its behavior over time.
Build a production RAG system that handles enterprise documents with advanced chunking, hybrid search, and automated evaluation using RAGAS.
Orchestrate multiple specialized agents under a supervisor using LangGraph's state machine architecture, with human-in-the-loop approval gates and parallel execution.
Build a scalable async document processing pipeline with Celery workers, Redis message brokering, and production-grade retry logic and dead-letter queues.
Take your AI system from local to cloud-deployed. Build async FastAPI endpoints, containerize with Docker, and deploy to AWS, GCP, or Azure with proper secrets management.
Add observability and evaluation to your capstone project. Monitor costs, trace agent decisions, write LLM-specific tests, and present your end-to-end production system.
Industry-standard tools used by AI engineering teams at top companies.
OpenAI GPT-4o, Anthropic Claude, Google Gemini, Open-source models
LangChain, LangGraph, LangServe, Agent protocols
FAISS, PGVector, Pinecone, ChromaDB, Redis
Celery, Redis, RabbitMQ, DynamoDB, Webhooks
FastAPI, LangServe, REST, WebSockets, gRPC
Docker, AWS, GCP, Azure, Terraform, GitHub Actions
Langfuse, LangSmith, OpenTelemetry, Prometheus
pytest, RAGAS, DeepEval, LLM-as-judge, Benchmarks
MCPs, A2A communication, Tool-use standards, Agent handoffs
This isn't a course. It's a system: curriculum, community, code, and career collateral.
Real feedback from engineers who went through the program.
"The RAG pipeline week alone was worth the entire price. I went from copy-pasting LangChain examples to understanding why my retrieval was failing and how to fix it. Deployed my first production RAG system two weeks after the bootcamp."
"Week 7 changed everything. I'd been building AI features locally for months but had no idea how to deploy them properly. Now I have three AI APIs running in production on AWS, and my company promoted me to lead our AI infrastructure team."
"The async systems module is what separates this from every other AI course. Understanding Celery, Redis, and dead-letter queues for AI workloads gave me the confidence to architect systems that actually handle real-world traffic."
"I was a junior dev with zero AI experience. The capstone project became the centerpiece of my portfolio. I showed it in interviews and got three offers within a month. The production focus is what made the difference — recruiters could see it was real."
"I've done other AI courses. The difference here is that everything deploys. Week 7's Docker + cloud deployment session finally connected all the pieces for me."
"The community alone is worth it. I debug with four other engineers from the cohort regularly. That kind of network doesn't come from watching YouTube videos."
AI Engineer & Educator · Founder, SoftBricks
Building production AI systems for enterprises across Africa and Europe. Architect behind StudyMate AI — a full-stack agentic platform handling onboarding, moderation, meeting scheduling, and support automation. Specializes in LangGraph multi-agent orchestration, RAG systems, and the infrastructure that makes AI actually work at scale. Every lesson in this bootcamp comes from real production experience, not theory.
This bootcamp is designed for a specific type of engineer. Make sure it's a fit.
One price. No upsells. Everything you need to go from zero to production AI engineer.
✓ Secure checkout · EMI available at checkout
Enroll now and get immediate access to full session recordings. Get ahead of your cohort, review the material at your own pace, and walk into the first live session already prepared. The earlier you start, the more you'll get out of the live experience.
Enroll Now — $399Got questions? We've got answers.
Plan for 10-15 hours per week. That includes 6 hours of live sessions (two 3-hour sessions on Saturday and Sunday) plus 4-9 hours for the weekly build project and self-study. The builds are where the real learning happens, so don't skip them.
No. You need solid Python skills and familiarity with APIs and web development. We teach the AI/LLM concepts from the ground up in Week 1. This is an engineering bootcamp, not a research program — we focus on building systems, not deriving loss functions.
All sessions are recorded and available within 24 hours. You also get unlimited re-attendance for future cohorts at no extra cost. Life happens — we've designed the program so you can catch up without falling behind.
Yes. Once you enroll, you get immediate access to full session recordings so you can start learning right away. Get ahead before the live cohort begins and walk into the first session already prepared.
We don't make job guarantees — anyone who does is lying. What we do give you is a portfolio of 8 production-grade projects, a capstone system you can demo in interviews, and the technical depth to pass AI engineering interviews. Our graduates consistently report that the portfolio projects are what got them hired.
Yes. EMI is available at checkout. We want cost to be the last reason someone doesn't join. Contact us at academy@softbricks.ai if you have any questions about payment options.
Live sessions are every Saturday and Sunday, 8:00 PM – 11:00 PM WAT. All sessions are recorded for anyone who can't attend live. The community and async support channels are active 24/7.
Three things: (1) Every week has a production build — not exercises, not notebooks, but deployed systems. (2) We cover the infrastructure that other courses skip: async queues, observability, cost monitoring, deployment. (3) The instructor builds production AI systems for a living. The curriculum comes from real client projects, not textbooks.