50+
companies actively hiring AI engineers right now
Amazon India
Amazon India
Stop learning about AI. Start building with it — RAG pipelines, agents, LLM apps, and a live deployed capstone. All in 12 weeks.
Amazon India
Amazon India
Organizations are no longer evaluating AI talent on awareness alone. They need people who can translate business problems into reliable AI workflows, connect models with data and APIs, deploy usable products, and evaluate whether the system is working.
Hiring managers now expect working systems, not just course completions. The bar has shifted from knowing concepts to demonstrating you can build, deploy, and explain real AI products.
Every week is tied to a working artifact. You progress from Python and ML foundations through production systems, finishing with a live capstone that anchors your portfolio.
Each project is designed to produce an artifact that can be reviewed, deployed, and discussed.
Four tightly sequenced modules covering Python, ML, LLMs, agents, and production deployment — everything required to build and ship end-to-end AI applications.
Your mentor has built AI products in production, not just academic research. You get guidance grounded in real engineering decisions, tradeoffs, and interview-ready explanations.
"The goal is not to chase every new model. The goal is to understand how to design, build, evaluate, and explain systems that solve real problems."Connect on LinkedIn
You won't just read about these tools — you'll use them to build, deploy, and monitor systems across the full stack, from LLM APIs and vector databases to cloud infrastructure and CI/CD.
You graduate with 15+ shipped projects, a live deployed capstone, a reviewed GitHub profile, and the ability to speak to every technical decision you made.
Everything you need to know about program structure, time commitment, what you'll build, and how mentorship and career support work.
Some exposure to Python is strongly recommended. Week 1 covers modern Python foundations, but students should be comfortable following code, debugging errors, and committing time to independent practice.
The program is project-led. Each week is tied to a working artifact, and the final phase is a live capstone that brings together architecture, implementation, deployment, evaluation, and presentation.
Self-paced courses are useful for information. This program is designed around execution: live cohort sessions, weekly project submissions, mentor review, production-oriented briefs, and a portfolio narrative that can be used in interviews.
Plan for 15–20 hours per week, including live sessions, project work, code review, and mentor interaction. The program is demanding because the outcome depends on consistent building, not passive attendance.
Students receive weekly 1:1 mentor touchpoints and project review. The focus is practical: code quality, architectural choices, debugging, portfolio direction, and how to explain technical decisions clearly.
Live participation is strongly recommended. Recordings are provided for missed sessions, but students are still expected to complete the weekly build and stay current with submissions.
A completion certificate is provided. The more important output is the portfolio: shipped projects, a deployed capstone, GitHub evidence, LinkedIn and resume positioning, and interview practice around your own work.
Students retain access to cohort materials, recordings, starter kits, and the alumni community. Curriculum updates are shared as the program evolves.
In weeks 10–12, you select a problem, define success criteria, write a technical design, build the first version, test it with users, deploy it publicly, and present it as the centerpiece of your portfolio.
Career support includes resume and LinkedIn review, portfolio critique, mock technical interviews, and guidance on presenting your AI projects. Where appropriate, strong students may be introduced to relevant opportunities.