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A job-ready curriculum built on software engineering + LLMs + AI agents + cloud + production skills.
To become a job-ready Agentic AI Engineer you need a combination of software engineering, LLMs, AI agents, cloud, and production skills. This program takes you through all of them — with agentic systems, RAG, and MCP at the core.
What makes this different from the usual online courses and bootcamps.
The entire program is delivered by a single practitioner — not split across different instructors. Every student learns at the same pace, hears the same voice, and is held to the same standard. No one falls through the cracks.
Most courses stop at prompting and RAG. This program goes all the way — agent architectures, MCP, evaluation pipelines, security, and production observability.
Every module maps to a hands-on project or practical exercise. You leave with real agents, real deployments, and real code — not just notes.
12 months of structured learning that maps directly to Agentic AI Engineer roles — not a certificate that sits on your LinkedIn collecting dust.
18 modules. Highlights marked with a star are the program's priority areas.
The language and tooling baseline you will use every day as an AI engineer.
The ML, deep learning, and NLP grounding you need before working with LLMs.
Using large language models as dependable building blocks in real systems.
Grounding models in real, up-to-date knowledge so answers come from your data.
Tools
The core skill area of this program — designing systems where models plan, act, and learn from outcomes.
Know one or two frameworks deeply rather than knowing everything superficially.
A very useful modern skill — connecting agents to external applications and data.
Automating real business processes end-to-end with agents and APIs.
SQL, NoSQL, and vector stores — and knowing when to reach for each.
Shipping reliable APIs and services that power your agents in production.
At least one major cloud platform, plus containers and deployment automation.
Getting models and prompts into production and keeping them healthy once there.
Tools
Becoming extremely important for production agents — measuring quality before users do.
Metrics
Understanding how agents get attacked — and how to harden them.
Answering the questions that keep agents safe: what did it do, why, how much it cost, and where it failed.
Working with images, speech, and documents — not just text.
The patterns senior engineers reach for when one agent is not enough.
An excellent Agentic AI Engineer is more than a coder — you need to frame problems before solving them.
This program is live on the platforms page — enroll to track your progress module by module.
Go to this program