IT & Software

AI/ML ENGINEER MASTER PROGRAM

Become a job-ready AI/ML Engineer with hands-on ML, Deep Learning, Generative AI, Agentic AI, and MLOps — powered by real projects, cloud pipelines, and industry mentorship.

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Created by Arjun Mehta English 0m 0 lecturesall levels

What you'll learn

The objective of this program is to equip learners with the technical depth, practical skills, and engineering mindset required to build, deploy, and scale modern AI systems used in real-world companies.
Develop a solid understanding of Python, statistics, linear algebra, and optimization essential for AI/ML.
Learn to work with real-world datasets using Pandas, NumPy, and data engineering workflows.
Gain hands-on experience with supervised, unsupervised, and ensemble learning techniques.
Understand ML system design, feature engineering, model evaluation, and performance optimization.
Build and train neural networks using TensorFlow and PyTorch.
Master CNNs, RNNs, LSTMs, Transformers, Vision Transformers, and Diffusion Models.
Apply deep learning to vision, NLP, forecasting, and generative tasks.
Understand the architecture of modern LLMs (GPT, LLaMA, Mistral).
Learn fine-tuning techniques (LoRA, QLoRA) and build domain-specific LLMs.
Implement RAG systems, vector databases, embeddings, and retrieval pipelines.
Build multi-agent AI systems with tool use, memory, and planning.
Build production-ready ML pipelines using CI/CD, Docker, Kubernetes, MLflow, DVC.
Deploy AI models on AWS, Azure, and GCP using cloud-native tools.
Implement model monitoring, drift detection, logging, and retraining pipelines.
Learn how real AI products are designed, architected, deployed, and scaled.
Understand vector DB optimization, inference cost reduction, and security best practices.
Build AI systems that meet enterprise standards for performance, reliability, and compliance.
Complete 10+ mini-projects across ML, DL, NLP, and Generative AI.
Build 4 enterprise-grade capstone projects showcasing end-to-end AI engineering.
Deploy solutions on cloud platforms and publish code on GitHub.

Requirements

  • To ensure a smooth and effective learning experience, learners enrolling in this program should have the following
  • Technical Requirements: A laptop with minimum 8GB RAM (16GB recommended for Deep Learning, LLM fine‑tuning, and cloud labs) Operating system: Windows / macOS / Linux Stable internet connection (minimum 10 Mbps) Ability to install software like Python, VS Code, Docker, Git, etc. Basic computer literacy (file handling, browsers, installations)
  • Learning Mindset: This program is hands-on and industry-focused. Learners should have: Curiosity to explore AI systems Willingness to practice consistently Commitment to complete labs and projects Openness to learning new tools and cloud platforms A problem-solving mindset

Description

The AI/ML Engineer Master Program by Tech Prowexa is industry‑aligned, job‑ready training program designed for students, working professionals, and career switchers who want to build deep expertise in Machine Learning, Deep Learning, Generative AI, LLMs, Agentic AI, and MLOps. This program goes far beyond traditional AI courses. It is engineered to match the expectations of top global companies, integrating multi-cloud deployment, AI product engineering, LLM fine‑tuning, RAG systems, and real-world capstone projects that mirror enterprise use cases. Learners progress from foundational math and Python to advanced ML, transformers, LLMs, multi-agent systems, and end‑to‑end MLOps pipelines. Every module includes hands-on labs, quizzes, mini-projects, and real datasets, ensuring practical mastery rather than theoretical understanding. By the end of the program, learners will be able to: Build and deploy ML and deep learning models Fine‑tune LLMs using LoRA/QLoRA Build RAG systems with vector databases Create multi-agent AI systems for automation Deploy AI solutions on AWS, Azure, and GCP Design scalable AI architectures for real-world products Implement MLOps pipelines with CI/CD, Docker, and Kubernetes Monitor, optimize, and maintain AI systems in production This program is designed to transform learners into full-stack AI engineers capable of building production-grade AI systems, not just classroom models.

Course Curriculum

0 sections • 0 lectures • 0m total length

About the Instructor

A

Arjun Mehta

UX Design Lead with 8+ years in product design. Has designed products used by millions worldwide.