Course Overview

If you are already working in the tech industry, you have seen the GenAI revolution firsthand. What began as impressive demos has become a fundamental shift in how machines interact with the world.

The change is profound. AI systems no longer just recognize patterns or predict outcomes; they now create entirely new content: detailed stories, convincing dialogues, functional code, and photorealistic images. This is not incremental progress. It is a leap from prediction to creation, and it has created unprecedented demand for engineers who can build, deploy, and optimize these systems.

Here is the challenge: landing a GenAI role requires a different preparation strategy than traditional ML positions. This course bridges that gap.

Who is this course for?

This course is designed for junior to intermediate artificial intelligence and machine learning (AI/ML) practitioners ready to transition into generative AI roles. Whether you are a recent graduate, a data scientist aiming to specialize, or a software engineer with ML fundamentals, you will find a clear, structured path forward.

What to expect at different experience levels

• 0 to 2 years: You are building your foundation in natural language processing (NLP) concepts, learning transformer architectures, and developing intuition for how GenAI systems work. Interviews at this level focus on theoretical understanding, basic implementation skills, and clear problem-solving communication.

• 2 to 5 years: You are expected to go deeper, demonstrating expertise in fine-tuning strategies, deployment patterns, and addressing real-world challenges. Interviewers look for evidence of end-to-end model development, optimization experience, and successful collaboration on projects.

Understanding the landscape helps set realistic expectations:

• Junior AI/ML engineers at established tech companies typically earn $90,000–$117,000 annually in the U.S.

• Intermediate-level positions range from $123,000 to $142,000.

• Senior roles at top firms can command salaries of $400,000 or more, reflecting the intense demand for experienced practitioners.

These figures vary significantly by location, company stage, and specialization.

Why are GenAI interviews different?

If you have interviewed for traditional data science or ML positions, you know the drill: logistic regression, decision trees, gradient boosting, hyperparameter tuning. Those fundamentals matter, but GenAI interviews move into new territory:

• Language and sequence modeling: Expect a discussion of transformer architectures (e.g., BERT, GPT, T5) and specialized NLP tasks, such as text generation, summarization, and dialogue systems. The focus shifts from structured data to how models handle language’s inherent ambiguity and context.

• Generative capabilities: Interviewers care less about classification accuracy and more about your ability to design systems that produce high-quality, original outputs. Can you build a system that generates coherent code, maintains conversation context across multiple turns, or creates images from text descriptions?

• Evaluation complexity: Forget simple accuracy metrics. You will navigate perplexity, BLEU, ROUGE, and increasingly, human preference ratings and alignment measures. Evaluating generation quality is fundamentally harder than classification, and interviewers know it.

• Deployment at scale: Expect deep dives into training and serving large models efficiently. How do you leverage distributed computing? When does quantization make sense? What are the tradeoffs between pruning and performance? These are practical considerations for production systems.

What does the AI/ML interview process look like?

While every company differs slightly, most GenAI interviews follow a predictable pattern:

1. Recruiter screen: Your first conversation focuses on background, motivation, and role alignment. Expect resume discussion and behavioral fundamentals.

2. Technical phone screen: A remote session testing your technical foundation. You might solve coding problems, discuss ML concepts, or explain past projects. This stage filters for problem-solving ability and core technical knowledge.

3. On-site interviews: The main event, typically spanning multiple hours or a full day:

• Coding interview: Standard algorithms and data structures assessment.

• AI fundamentals: Deep technical questions on concepts underlying modern GenAI.

• System design: Designing scalable ML systems end-to-end.

• Behavioral interview: Exploring your experiences, collaboration style, and approach to challenges.

After these rounds, the team evaluates your technical depth and cultural fit before extending an offer.

Note: 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 readiness.

What does a modern AI/ML engineer do?

Titles vary by company, but AI/ML engineers often emphasize production deployment and system integration. Some ML engineer roles focus more heavily on model development and experimentation.

Here’s a practical definition:

How this differs from related roles:

• Research scientists: Push the state of the art—inventing algorithms, exploring novel architectures, and publishing papers.

• AI/ML engineers: Apply proven techniques to solve real problems at scale.

• ML engineers (role-specific): Often focus on model training and experimentation; AI/ML engineers emphasize production integration and system reliability.

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

• Integrate models into real-world systems, such as recommendation engines, chat interfaces, fraud detection tools, and generative tools (e.g., code generation or image synthesis).

• Scale model training and inference using cloud platforms, GPUs/TPUs, and distributed computing.

• Build and maintain data pipelines that feed models with quality inputs.

• Monitor and improve model performance in production.

• Collaborate with data scientists, backend engineers, and product managers to ship AI features.

How to use this course?

This is not a “memorize these answers” course. Every lesson is built around the exact questions interviewers ask, and the headings themselves are those questions.

Instead of treating a topic abstractly, we present it the way an interview unfolds: one question at a time, from foundational prompts to more advanced follow-ups. Each lesson begins with a brief introduction to the concept, followed by a real interview question posed under each heading. Inside that section, we walk through the full explanation first—intuition, mechanics, and technical detail—just as you would do when answering thoughtfully in an interview.

After the detailed explanation, you’ll often see the supporting components:

• Quick answer: A 20–40 second polished version you can speak confidently.

• Educative byte: Short industry insights, historical notes, or practical realities interviewers appreciate.

• Interview traps: Questions that the interviewer might ask purposely to see if you are able to avoid common mistakes.

• Common variants: Different ways the same question might be phrased.

• Optional drills: Short scenarios that help you strengthen depth and adaptability.

By structuring lessons as a sequence of real questions, we mirror how interviewers actually explore your understanding: beginning with fundamentals, then probing deeper as you demonstrate competence. You build both the conceptual depth and the communication clarity needed in modern GenAI interviews.

Why go deep when this is an interview course?

You might ask, “If this is interview prep, why not just give simplified answers you can memorize?”

Here is the reality: AI/ML interviews emphasize deep theoretical understanding. Unlike many software engineering interviews, where pattern recognition sometimes suffices, AI interviewers probe whether you truly grasp what is happening under the hood.

Some concepts are inherently complex because they solve complex problems. We do not avoid clear explanations—they are an excellent starting point. But years in this field have taught us:

• Oversimplification strips away critical nuances.

• Surface-level understanding creates an illusion of mastery that crumbles under interview pressure.

• As you advance, you will not find beginner-friendly explanations for cutting-edge techniques.

• The only path forward is building comfort with technical depth.

When an interviewer asks, “How does layer normalization differ from batch normalization in transformers?” they are looking for evidence that you understand why that design choice matters for training stability and performance.

We are building a crucial habit: Reading and understanding detailed technical content—dense research papers, poorly documented codebases, and deep technical explanations. This course develops that skill now, in a structured environment, rather than leaving you to struggle during interviews or on the job.

Our promise: We meet you where you are, then take you deeper. Each concept starts accessibly and does not stop at the shallow end. By the end of each lesson, you will have the depth to handle both straightforward questions and the nuanced follow-ups that separate strong candidates from exceptional ones.

What’s expected of you?

To get the most from this course, you should have foundational knowledge in:

• Mathematics and statistics: Comfort with probability, linear algebra, and basic calculus—enough to understand how models optimize and learn.

• ML and deep learning basics: Familiarity with overfitting, loss functions, backpropagation, and neural network architectures.

• NLP fundamentals: Experience with tokenization, text preprocessing, and embedding methods such as Word2Vec, GloVe, and TF-IDF.

• Python fluency: Ability to write clear Python code using libraries like NumPy and frameworks such as PyTorch or TensorFlow.

What you’ll achieve

By the end of this course, you will have mastered:

• Fundamental NLP concepts and their evolution into modern GenAI.

• Deep learning architectures with emphasis on transformers.

• Advanced topics: attention mechanisms, positional embeddings, RLHF, and tokenization strategies.

• Evaluation frameworks for generative models.

• Deployment considerations: quantization, pruning, inference optimization, and knowledge distillation.

• Ethical dimensions: bias mitigation, adversarial robustness, and prompt injection defenses.

More importantly, you will gain the ability to explain these concepts clearly and confidently—the skill that ultimately determines interview success.

You will be well-prepared for interviews that require technical depth, clarity on advanced methods, and practical insights into real-world deployment. Not just ready to answer questions, but ready to engage in the kind of technical discussions that lead to offers.

Let’s get started.