AI / GenAI Engineer Interview Kit

Overview
Curriculum
  • 22 Sections
  • 112 Lessons
Collapse All
Statistics & Mathematics for AI
5 Lessons
  1. 1. Probability Theory & Bayesian Thinking
  2. 2. Linear Algebra for Machine Learning
  3. 3. Optimization & Gradient Descent
  4. 4. Loss Functions & Activation Functions
  5. 5. Deep Learning Mathematics
Machine Learning Fundamentals
8 Lessons
  1. 1. Supervised vs Unsupervised Learning
  2. 2. Feature Engineering & Preprocessing
  3. 3. Overfitting, Underfitting & Regularization
  4. 4. Model Evaluation
  5. 5. Hyperparameters & Optimization
  6. 6. Ensemble Methods
  7. 7. Model Complexity & Dataset Considerations
  8. 8. Production ML & System Design
Deep Learning & NLP Foundations
6 Lessons
  1. 1. Neural Network Fundamentals
  2. 2. Scale & Architecture
  3. 3. CNN, RNN, and Transformer Architectures
  4. 4. Text Tokenization & Embeddings
  5. 5. NLP Tasks & Models
  6. 6. Modern NLP & Transformers
Transformers: Architecture & Training
8 Lessons
  1. 1. Core Transformer Architecture
  2. 2. Encoder, Decoder and Encoder-Decoder Models
  3. 3. Attention Mechanisms
  4. 4. Positional Encoding and Sequence Representation
  5. 5. Pooling and Sequence Representation
  6. 6. Training Objectives and Language Modeling
  7. 7. Mixture of Experts Transformers
  8. 8. Practical Training Challenges
Large Language Models (LLMs)
4 Lessons
  1. Model Behavior & Generation Control
  2. Tokenization, Context and Cost
  3. Model Selection and Optimization
  4. Scenario-Based LLM Questions
Prompt Engineering
4 Lessons
  1. Core Prompting Concepts
  2. Prompt Structure and Design Thinking
  3. Prompting for RAG and Agent Systems
  4. Senior-Level Prompt Engineering
Fine-Tuning & Model Adaptation
5 Lessons
  1. Core Concepts
  2. PEFT, LoRA and Efficient Methods
  3. Data and Training Strategy
  4. Alignment and Reinforcement Methods
  5. Production and Decision-Making
RAG Fundamentals & Core Components
12 Lessons
  1. Data Ingestion
  2. Data Preprocessing
  3. Chunking
  4. Embeddings
  5. Vector Database
  6. Query Expansion
  7. Retrieval
  8. Hybrid Search and Re-Ranking
  9. Generation
  10. Memory Management & Caching in RAG System
  11. Evaluation
  12. Guardrails and Prompting
RAG Frameworks, Tools & Implementation
5 Lessons
  1. LangChain (Architecture and Components)
  2. LlamaIndex (Indexing and Retrieval Design)
  3. Vector Databases and Search Engines
  4. Evaluation Frameworks
  5. LangGraph(Stateful and Agentic Workflows)
Production RAG Systems & Architecture
4 Lessons
  1. Production RAG Systems & Architecture
  2. Scalability and Throughput
  3. Observability and Monitoring
  4. Scenario-Based Questions
Use Case 1: Legal and Compliance RAG Systems for Regulatory Knowledge
4 Lessons
  1. System Design & Architecture
  2. Retrieval and Accuracy
  3. Risk and Guardrails
  4. Evaluation and Production
Use Case 2: Healthcare Knowledge RAG Systems for Medical Information Retrieval
4 Lessons
  1. Architecture & Data
  2. Safety and Risk
  3. Retrieval and Evaluation
  4. Scalability
Use Case 3: Enterprise Knowledge Base RAG Systems for Internal Organizational Knowledge
3 Lessons
  1. System Design
  2. Retrieval and Query Handling
  3. Production and Monitoring
RAG (The End)
Agent Fundamentals & Architecture
11 Lessons
  1. Core Understanding of Agents
  2. Reactive vs Deliberative Agents
  3. Tool-Calling Agents
  4. ReAct Pattern and Reasoning Loops
  5. Planner-Executor Architecture
  6. Memory in Agents
  7. Reflection and Self-Correction
  8. Single-Agent vs Multi-Agent Systems
  9. Control and Safety Fundamentals
  10. Design Thinking and Trade-Offs
  11. Memory and Caching
Agent Frameworks & Ecosystem
6 Lessons
  1. LangChain Agents
  2. LangGraph
  3. CrewAI
  4. AutoGen
  5. Google Agent Development Kit (ADK)
  6. Model Context Protocol (MCP)
Agent Production Systems
5 Lessons
  1. Performance & Latency Optimization
  2. Cost Optimization & Budget Control
  3. Reliability & Failure Handling
  4. Observability & Monitoring
  5. Scalability & Infrastructure
Agent Use-Case Design (Scenario-Based Questions)
3 Lessons
  1. Use Case 1: Autonomous Data Analysis Agent
  2. Use Case 2: AI Research & Report Generation Team
  3. Use Case 3: Enterprise Knowledge Assistant (Agentic RAG)
Agents(The End)
Memory, Databases & Caching for GenAI Systems
5 Lessons
  1. 1. Memory Design in AI Systems
  2. 2. Databases for AI Systems
  3. 3. Vector Databases & Retrieval Infrastructure
  4. 4. Caching in GenAI Systems
  5. 5. Memory & Caching for Agent Systems
Backend, FastAPI & Deployment
4 Lessons
  1. 1. FastAPI Architecture
  2. 2. LLM Serving APIs
  3. 3. Deployment & Infrastructure
  4. 4. Production Operations
GenAI System Design
6 Lessons
  1. Architecture Design for GenAI Systems
  2. Scalability & Performance Engineering
  3. Cost Optimization for GenAI Systems
  4. Observability, Logging & Monitoring
  5. Reliability, Safety & Guardrails
  6. Data & Model Lifecycle Management

Artificial Intelligence and Generative AI are rapidly transforming how modern software systems are built. Today, companies expect engineers not only to understand machine learning models but also to design end-to-end AI systems using LLMs, RAG architectures, and AI agents.

Interviews for AI and Generative AI roles now focus on both foundational knowledge and practical system design skills. Candidates are often asked questions about model architectures, retrieval systems, vector databases, prompt design, and increasingly about agent-based AI systems that can reason, use tools, and perform multi-step tasks.

The AI / GenAI Engineer Interview Kit is designed to help you prepare for these modern interviews. This course brings together 500+ structured interview questions and explanations covering the full stack of knowledge required for AI and Generative AI engineering roles.

From core AI fundamentals to modern LLM architectures and agent-based systems, this interview kit helps you build the conceptual clarity needed to confidently handle technical discussions in real industry interviews.

Who This Course Is For

This course is designed for learners preparing for AI and Generative AI engineering interviews, including:

  • AI Engineers
  • Machine Learning Engineers
  • Generative AI Engineers
  • LLM Engineers
  • AI Agent Developers
  • Software Engineers transitioning into AI roles
  • Students preparing for AI / ML interviews

It is especially useful for professionals who want to understand how modern AI systems are designed and deployed in production environments, including systems powered by LLMs, RAG pipelines, and AI agents.

What This Course Covers

This interview kit covers the major topics that companies expect candidates to understand when building modern AI and generative AI systems.

AI and Machine Learning Foundations

  • Statistics for AI
  • Machine Learning fundamentals
  • Deep Learning concepts
  • Natural Language Processing (NLP)

Modern Generative AI Concepts

  • Transformers architecture
  • Large Language Models (LLMs)
  • Prompt engineering techniques
  • Model fine-tuning strategies

Generative AI System Architecture

  • Retrieval-Augmented Generation (RAG)
  • Vector databases and embeddings
  • Memory systems for AI applications
  • Caching strategies for LLM systems

AI Agents and Intelligent Workflows

  • AI agent architectures
  • Tool usage and reasoning loops
  • Multi-step agent workflows
  • Multi-agent collaboration systems

Building and Deploying AI Applications

  • Generative AI system design
  • API-based AI architectures
  • FastAPI deployment for AI applications
  • Production considerations for AI systems

Value You Will Gain From This Course

By completing this course, you will:

  • Practice with 500+ AI and Generative AI interview questions
  • Strengthen your understanding of AI, ML, and deep learning fundamentals
  • Learn how LLM-based systems are designed and built
  • Understand the architecture behind RAG systems and vector search
  • Gain exposure to AI agent architectures and workflows
  • Learn how to design production-ready AI systems
  • Improve your ability to explain complex AI concepts during interviews

This interview kit is designed to help you build the knowledge and confidence needed to succeed in interviews for AI Engineer, Machine Learning Engineer, Generative AI Engineer, and AI Agent Developer roles.

Deleting Course Review

Are you sure? You can't restore this back

Course Access

This course is password protected. To access it please enter your password below:

Scroll to Top