Mentor-led, not content-led
You learn directly from an experienced AI Engineer who has worked across Data Science, MLOps, and GenAI systems.
- Clear weekly roadmap
- Live implementation support
- Career transition strategy
For Software Engineers Transitioning to AI Engineering
Move from software engineer to AI engineer with real production skills.
If you already code but feel stuck with random AI tutorials, this is for you. You get a clear mentor-led path, hands-on system design, live workshops, and career transition guidance.
10+ Years
Industry Experience
750+
Learners Mentored
4.8 / 5
Learner Rating
You are not behind. You are just missing a clear system.
Most engineers do not fail because they cannot learn AI. They fail because the path is noisy.
Stuck in current role
You are good at backend or frontend, but not sure how to position yourself for AI roles.
Too many resources
You keep watching tutorials, but still do not know what to build first and why.
No real-world exposure
You have notebooks, but not production-style AI systems you can confidently discuss.
Why trust this program?
You learn with someone who has built and taught this in the real world.
I am Dr. Arun Kumar. I have spent over a decade working across Data Science, MLOps, and GenAI systems. I have mentored 750+ learners and professionals.
My focus is simple: help software engineers build practical AI capability that translates into interviews, projects, and actual job outcomes.
PhD with applied industry background
Experience across ML pipelines and deployment
Mentor-first teaching style
Clarity, systems thinking, execution
A practical AI Engineering transition system
This is not theory-heavy. It is build-focused and outcome-driven.
01
Foundation to application
Learn what matters and start implementing immediately.
02
End-to-end systems
Go from data and model to deployment and monitoring.
03
Engineering mindset
Use reproducibility, evaluation, and reliability in every project.
04
Career translation
Turn projects into role-ready proof for interviews.
What you will learn and build
Everything is tied to real-world AI Engineering outcomes.
AI Engineering Core
Model lifecycle, data workflow decisions, evaluation, and system design fundamentals.
LLM and GenAI Systems
Prompting, retrieval patterns, orchestration, and guardrails for practical products.
MLOps and Deployment
Experiment tracking, serving, observability, and iteration loops for production.
Portfolio Projects
Build portfolio-grade projects aligned with real engineering constraints.
Program Includes
- Live workshops and implementation sessions
- Structured weekly roadmap
- Career transition guidance
- Portfolio and interview positioning support
What learners say
Real feedback from engineers who wanted practical confidence, not more theory.
“I had ML basics but no deployment confidence. This helped me ship complete workflows and explain them clearly in interviews.”
“The structure changed everything. I stopped guessing and started building with purpose every week.”
“I finally understood AI Engineering beyond notebooks. Project feedback was the biggest differentiator.”
Next cohort starts soon
Seats are intentionally limited so feedback stays personal and implementation-focused.
Next live batch: March 2026 • Limited seats: 30
Frequently asked questions
I am a software engineer, not an ML specialist. Can I still join?
Yes. This program is designed for engineers transitioning into AI roles. We build from your software background.
Is this only theory?
No. The program is implementation-first with projects, systems thinking, and feedback.
Will I get career transition support?
Yes. You get guidance on role mapping, portfolio positioning, and interview communication.
How is this different from generic AI courses?
Generic courses teach isolated concepts. This program teaches how to build and ship end-to-end AI systems.
How much time should I commit every week?
We recommend 6–8 focused hours per week for strong outcomes.