Generative AI Engineering: Master Mock Interviews
Affiliate link — we may earn a commission. Learn more
Generative AI Engineering: Master Mock Interviews by Himanshu Kaushik is a hands‑on Udemy course that lets you learn generative AI online through realistic interview‑style challenges. Updated July 2026, it targets anyone searching for a free generative AI course, a Generative AI Udemy course, or a free mock interview training in AI engineering. The program focuses on building production‑ready Retrieval‑Augmented Generation (RAG) pipelines, autonomous LLM agents, and robust MLOps practices, preparing you for enterprise‑grade AI roles and certification‑ready interviews.
What You'll Learn
- Build end‑to‑end Retrieval‑Augmented Generation (RAG) systems with vector‑DB filtering and re‑ranking models.
- Master autonomous LLM agents using ReAct prompting, function calling, and Chain‑of‑Thought (CoT) techniques.
- Learn model alignment strategies, including catastrophic forgetting mitigation and PEFT/QLoRA fine‑tuning on 70B‑parameter models.
- Understand advanced MLOps concepts such as GGUF quantization, vLLM, and PagedAttention for scalable deployment.
- Create secure, hallucination‑free AI applications by applying prompt injection defenses and jailbreak prevention.
- Implement streaming token responses with Server‑Sent Events (SSE) and edge‑device deployment workflows.
- Analyze real‑world engineering scenarios across four randomized exam sets, each containing 200 practical challenges.
- Apply best‑practice testing methods to validate your readiness for senior generative‑AI engineering interviews.
Course Details
- Instructor: Himanshu Kaushik
- Level: Intermediate to Advanced
- Language: English (en‑US)
- Enrolled students: 9,397
- Last updated: July 2026
- Certificate: Yes, upon completion
- Includes: Lifetime access, mobile‑friendly video, downloadable resources
What This Course Covers
Information Retrieval & RAG
- Evaluate architectural strategies for Retrieval‑Augmented Generation, including vector‑DB filtering.
- Solve the “Lost in the Middle” phenomenon by optimizing dense vector searches.
- Implement re‑ranking models to improve relevance and reduce hallucinations.
- Design scalable RAG pipelines that handle millions of queries per day.
Prompt Engineering & Autonomous Agents
- Build LangChain agents using ReAct prompting and function calling.
- Apply Chain‑of‑Thought (CoT) reasoning to complex multi‑step tasks.
- Prevent adversarial jailbreaks and prompt injections with robust safety layers.
- Orchestrate autonomous workflows that integrate external APIs and data sources.
Model Fine‑Tuning & Alignment
- Fine‑tune 70B‑parameter open‑source models on consumer hardware with QLoRA.
- Conduct parameter‑efficient fine‑tuning (PEFT) to preserve core knowledge.
- Align models using Reinforcement Learning from Human Feedback (RLHF) for safety.
- Address catastrophic forgetting through continual learning techniques.
MLOps, Quantization & Deployment
- Optimize LLM deployment with GGUF quantization for reduced memory footprints.
- Deploy models using vLLM and PagedAttention to maximize throughput.
- Stream token responses via Server‑Sent Events (SSE) for real‑time applications.
- Deploy quantized models to edge devices, ensuring low‑latency inference.
Interview Simulation & Assessment
- Tackle four randomized exam sets, each containing 200 scenario‑based challenges.
- Receive instant feedback on architectural decisions and coding implementations.
- Benchmark performance against industry standards for AI engineering roles.
- Track progress with built‑in analytics to identify knowledge gaps.
Career‑Focused Project Work
- Develop a production‑grade chatbot that scales to millions of users.
- Build a secure enterprise AI system that complies with data‑privacy regulations.
- Create a portfolio of mock‑interview solutions to showcase to recruiters.
- Prepare for senior AI‑engineer interview questions commonly asked by top tech firms.
Who Should Take This Course
- Software engineers transitioning to generative‑AI application development with LangChain or LlamaIndex.
- Machine‑learning engineers seeking mastery of LLM deployment, fine‑tuning, and quantization.
- Solutions architects who need to design secure, hallucination‑free enterprise AI systems.
- AI researchers preparing for senior‑level interview panels at leading tech companies.
- Professionals aiming for a certification‑ready portfolio in generative‑AI engineering.
Prerequisites
- Proficiency in Python programming and familiarity with basic machine‑learning concepts.
- Experience with REST APIs and cloud‑based development environments (e.g., AWS, Azure).
- Understanding of neural‑network fundamentals is recommended but not mandatory.
Why Enroll in This Course
This course delivers a battle‑tested interview environment that mirrors real‑world AI engineering challenges, giving you a decisive edge over competitors. A free coupon provides 100 % off for a limited time, making the full‑featured Udemy experience accessible without cost. Enroll now—offers expire soon, and the skills you acquire will stay relevant as generative AI continues to dominate the industry.
Course Highlights
- Lifetime access to all video lectures, quizzes, and practice exams.
- Self‑paced learning allows you to progress on your own schedule.
- Certificate of completion to showcase on LinkedIn and resumes.
- 30‑day money‑back guarantee for risk‑free enrollment.
- Mobile‑friendly content lets you study on smartphones or tablets.
- Real‑world mock interviews with instant feedback and detailed solutions.
Frequently Asked Questions
Q: Is this course really free?
A: Yes. A free Udemy coupon grants 100 % off the regular price, giving you full access to all materials at no cost while the coupon remains valid.
Q: What will I learn in this Generative AI course?
A: You will master RAG architecture, autonomous LLM agents, advanced model fine‑tuning, alignment techniques, and production‑grade MLOps practices—all through realistic interview scenarios.
Q: Do I get a certificate after completing this course?
A: A Udemy‑issued certificate of completion is awarded once you finish all modules and pass the mock‑interview assessments, which you can add to your professional profile.
Q: Is this course suitable for beginners?
A: The program is designed for intermediate to advanced learners; however, motivated beginners with solid Python and ML basics can still benefit from the structured curriculum.
Q: How long do I have to enroll for free?
A: The free coupon is available for a limited period; it typically expires after a few weeks, so you should claim it promptly to secure the 100 % discount.
Final Thoughts
Generative AI Engineering: Master Mock Interviews equips software engineers, ML specialists, and solutions architects with the practical expertise required to ace senior AI‑engineering interviews and build production‑grade generative AI systems. Grab the free coupon today, dive into the hands‑on labs, and launch your career as a confident AI engineer.
Affiliate link — we may earn a commission
Affiliate link — we may earn a commission. Learn more
