Course Overview

If you’ve been paying attention to technology news lately, you’ve likely noticed that generative AI (or GenAI) is making headlines everywhere—and for good reason. GenAI isn’t just another trendy buzzword; it’s transforming how machines interact with the world.

Traditionally, AI models were built to classify images, predict outcomes, or detect patterns. Now, with GenAI, machines are no longer just recognizing what’s already there—they’re creating entirely new things: detailed stories, convincing dialogues, unique images, and even fully functional code. It’s a shift from prediction toward genuine creation. This explains why startups and tech giants are rushing to leverage GenAI, creating countless opportunities for engineers and developers.

Who is this course for?

This course is designed for junior to intermediate AI/ML practitioners aiming to transition into generative AI (GenAI) roles. Whether you’re a recent graduate, a data scientist, or a software engineer with foundational machine learning knowledge, this course will guide you through the specialized skills and concepts essential for GenAI interviews.

What to expect at different experience levels:

• 0–2 years of experience: Learning core NLP concepts, understanding transformer architectures, and building a foundational understanding of GenAI principles. Emphasis on theoretical knowledge, basic model implementation, and problem-solving skills is required.

• 2–5 years of experience: Deepening expertise in model fine-tuning, deployment strategies, and addressing real-world challenges in GenAI applications. They should be able to demonstrate experience with end-to-end model development, optimization techniques, and collaborative projects.

Knowing potential earnings helps you set realistic career expectations:

• Junior AI/ML engineers: Average annual salaries in the U.S. range from $90,000 to $116,888, depending on location and company size.

• Intermediate AI/ML engineers: Salaries typically range from $122,619 to $141,720 annually, with variations based on expertise and industry.

• Senior roles: Positions like staff AI/ML engineers at top firms can command salaries up to $400,000, reflecting the high demand for experienced professionals in the field.

Note: Salaries vary based on location, company, and individual experience.

Why are GenAI interviews different?

Questions usually revolve around familiar topics like logistic regression, decision trees, gradient boosting, or tuning hyperparameters if you’ve interviewed for typical data science or machine learning positions. Those skills are important, but GenAI roles require a deeper dive into:

• Language and sequence modeling: You’ll encounter powerful NLP frameworks like Transformers—BERT, GPT, T5—and specialized NLP tasks such as text generation, summarization, and sentiment analysis.

• Generative capabilities: Interviewers now look beyond your ability to classify data—they’re interested in how you can design systems that generate high-quality, original outputs (like meaningful dialogues or precise code).

• Evaluation: Instead of just accuracy or F1-score, you’ll discuss metrics specifically designed for evaluating generative models, like perplexity, BLEU, ROUGE, and even metrics based on human feedback and alignment.

• Deployment and scaling: Interviews often dig into your understanding of how to efficiently train, fine-tune, and deploy large-scale models using distributed computing, GPUs/TPUs, quantization, and pruning.

What does the AI/ML interview process look like?

Understanding the typical interview process can help you prepare effectively. Here’s an overview of the common stages:

1. Recruiter screen

An initial conversation to assess your background, interest in the role, and alignment with the company’s needs. This may include discussions about your resume and general behavioral questions.

2. Technical phone screen

A remote interview focusing on your technical skills. You might be asked to solve coding problems, discuss machine learning concepts, or explain past projects. This stage evaluates your problem-solving abilities and technical knowledge.

3. On-site interviews

A series of in-depth interviews, often conducted over a day, covering various aspects:

• Coding interview: Assessing your proficiency in algorithms and data structures.

• AI fundamentals: Test your understanding of the foundational concepts that are the basis of modern GenAI.

• System Design: Evaluating your ability to design scalable ML systems.

• Behavioral interview: Exploring your experiences, teamwork, and problem-solving approaches.

These interviews aim to gauge your technical expertise and cultural fit within the company. After completing the interview rounds, the hiring team reviews your performance to make a decision. If successful, you’ll receive an offer outlining the role, compensation, and other details.

Remember: Along with AI/ML-specific questions, preparing for general coding and behavioral interviews is important. We recommend supplementing your preparation with dedicated coding interview resources to strengthen your readiness.

What does a modern AI/ML engineer do?

While the AI/ML engineer role is still evolving, it generally refers to a practitioner-focused position that blends software engineering with machine learning expertise. These hands-on roles often involve deploying and integrating AI systems into real-world products.

Here’s a practical definition:

You may ask how it is different from other traditional roles already there. An easy-to-follow comparison is:

• AI/ML engineer vs. research scientist: Research scientists primarily focus on advancing the state of the art, creating new algorithms, or exploring novel architectures. AI/ML engineers, on the other hand, apply existing models to solve real-world problems at scale.

• AI/ML engineer vs. ML engineer: While there’s some overlap, ML engineers typically focus more on training and fine-tuning models. AI/ML engineers integrate those models into scalable, production-ready systems.

This means that the responsibilities of an AI/ML engineer are:

• Integrating models into real systems like recommendation engines, chat interfaces, fraud detection tools, and generative tools (e.g., code generation or image synthesis).

• Working with cloud platforms, GPUs, or distributed systems to train and scale models efficiently.

• Using best practices to continuously deliver reliable AI performance in production environments.

• Preparing, transforming, and managing data pipelines to feed models with high-quality inputs.

• Partnering with data scientists, backend engineers, and product managers to bring AI features to life.

How will we help you?

This course will give you exactly what you’ll need to successfully navigate an interview for a GenAI-oriented ML engineering position. We’ll cover questions related to:

• Fundamental NLP concepts

• Deep Learning and neural architectures

• Advanced GenAI-specific topics like attention mechanisms, positional embeddings, RLHF, and tokenization strategies

• Model evaluation strategies

• Real-world deployment considerations like quantization, pruning, inference optimization, and distillation

• Ethical considerations like bias mitigation, adversarial attacks, and prompt injection

By the end, you’ll be prepared for interviews that demand strong technical fundamentals, clarity around advanced generative methods, and practical insights into real-world deployment and operations.

What’s expected of you?

To ensure success, you should come into this course with a foundation in these areas:

• Mathematics and statistics: Basic comfort with probability, linear algebra, and calculus to grasp how models optimize and learn.

• Machine learning and deep learning basics: Overfitting, loss functions, neural architectures.

• NLP fundamentals: Tokenization, text preprocessing, embedding methods (Word2Vec, GloVe), and TF-IDF.

• Python fluency: Writing clear, understandable Python code comfortably using libraries like NumPy and frameworks like PyTorch or TensorFlow.

Don’t worry if you’re rusty—we’ll gently refresh key concepts throughout the course as we cover the material needed to confidently tackle the most relevant interview questions.

By the end of the course, you’ll gain a deeper understanding of everything from classical NLP foundations to advanced transformers, deployment at scale, and ethical considerations. You’ll become proficient in understanding—and clearly explaining—the most relevant concepts for GenAI interviews.

We’re excited to embark on this journey with you. Let’s get started!