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AI Engineer Interview Prep

Crack top AI & GenAI interviews with our expert-led course covering DSA, System Design, ML, LLMOps, and Real-World Projects. Designed for roles like AI/ML Engineer, GenAI Specialist, and AI Solution Architect.

Get full access to a role-specific roadmap, level-based syllabus (0–2, 2–5, 5–8 years), and advanced prep content. Book a free session, check the fees, or download the full curriculum now.

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Inquire about AI Interview Prep

Why Join Our AI Engineer Interview Course?

Crack Product AI Interviews with Confidence

Master AI interview patterns, frameworks, and deployment practices expected at top tech companies like Google, Apple, and Fractal.

Real-World Agent System Projects

Build multi-agent projects including RAG tutors, sales bots, and memory-based assistants used in modern enterprise stacks.

System Design for AI Workflows

Design scalable AI solutions—covering APIs, infra, vector DBs, rate limits, caching, and failover logic.

Hands-On with LangGraph & AutoGen

Learn DAG workflows, tool routing, retry/fallback, and task orchestration using LangGraph and AutoGen.

Master Prompt Engineering & RAGOps

Gain deep expertise in prompt chaining, memory-based agents, CoT, ReAct, ToT, and smart context management.

MCP + Secure Deployment Patterns

Use Model Context Protocol (MCP) for secure agent deployment, sandboxed tools, and context-aware control.

AgentOps, Observability & Cost Control

Set up tracing, LangSmith, Prometheus, OpenTelemetry, and fallback strategies to ensure robust infra.

SDKs & Infra Tools You’ll Actually Use

Learn the toolchain behind production AI systems: OpenAI SDK, Google A2A, CrewAI, vLLM, Qdrant, and more.

Mentorship from Top AI Engineers

Weekly guidance and mock interviews from engineers working on agentic AI, LLM infra, and real-world GenAI.

Explore Our Core AI Courses

Generative AI Specialization

Advanced GenAI Systems

Master Transformers, RAG, Diffusion, and deploy real-world GenAI solutions.

Large Language Model (LLM) Mastery

LLMs from Scratch

Understand Transformer internals, fine-tuning, quantization, and serving LLMs.

Agentic AI Mastery

Multi-Agent Workflows

Learn LangGraph, AutoGen, CrewAI, and build multi-agent GenAI systems.

GenAI for Developers

Build GenAI Apps

Use OpenAI, Gemini, and Cohere APIs to build AI apps with FastAPI and Streamlit.

Skills You’ll Gain in the AI Interview Course

0–2 Years

  • Problem Solving with DSA (Python)
  • OOPs, File & Exception Handling
  • Basic Git & Version Control
  • Machine Learning Foundations
  • Model Evaluation & Metrics
  • Fundamentals of Neural Networks
  • FastAPI Basics & API Building
  • Basic Docker & Deployment
  • LangChain Introduction

2–5 Years

  • System Design for ML & AI
  • ML & DL (CNN, RNN, Transformers)
  • Data Versioning & Experiment Tracking
  • Model Serving & CI/CD
  • Vector DBs & Embeddings
  • LLMOps Fundamentals (LangChain, LlamaIndex)
  • API Rate Limiting & Caching
  • Prompt Engineering & RAG
  • Fine-Tuning & Evaluation Pipelines

5–8 Years

  • End-to-End AI System Design
  • Agentic Architectures & PromptOps
  • Multimodal Retrieval Strategies
  • Production AI Infra (MLflow, SageMaker)
  • Model Governance & Drift Handling
  • Secure API Integrations (OAuth, MCP)
  • LangSmith, LangFuse Observability
  • Cost-Aware Model Deployment
  • Cloud Scaling & Hybrid Workflows

Tools & Frameworks for AI Engineer Interviews

Python

Core Programming

Build AI workflows, scripts, and backend logic.

PyTorch

ML/DL Framework

Train neural networks and fine-tune models.

Scikit-learn

ML Library

Use standard ML models for classification, regression, and clustering.

Git

Version Control

Manage and collaborate on codebases with Git workflows.

Docker

Containerization

Build, run, and deploy AI apps consistently across environments.

FastAPI

API Framework

Create high-performance APIs for AI model deployment.

Redis

Fast Cache & Messaging

Power real-time AI pipelines with queues and memory stores.

MongoDB

NoSQL Database

Store AI results, user logs, and embeddings efficiently.

MLflow

Model Tracking

Track experiments, log parameters, and version your models.

AI Engineering Interview Preparation Curriculum

Python for AI Engineering
Python Foundations
  • File Handling (Text, CSV, JSON)
  • Exception Handling (try/except/finally)
  • OOP (classes, inheritance, magic methods)
  • Multithreading & Multiprocessing
  • Time & Space Complexity (Big-O)
Data Structures & Algorithms
Core DSA Patterns
  • Sorting: Bubble, Merge, Quick
  • Searching: Linear, Binary
  • Two Pointer & Sliding Window
  • Linked List: Reversal, Cycle Detection
Databases for AI
SQL + NoSQL + Vector DBs
  • SQL Joins, Aggregation, Indexing
  • MongoDB Aggregation Framework
  • FAISS basics, similarity search
API Development + DevOps
FastAPI for ML Deployment
  • REST API Design
  • Pydantic Input Validation
  • Model Inference, Swagger Docs
DevOps Essentials
  • GitHub Flow
  • Docker Images, Containers
  • Version Control for ML
Machine Learning
ML Fundamentals
  • Linear & Logistic Regression
  • SVM, Decision Trees, Random Forests
  • Boosting: AdaBoost, Gradient Boosting
  • Evaluation Metrics: MSE, RMSE, AUC-ROC
  • Cross-validation, Optuna
  • PCA, K-Means, SMOTE
Deep Learning & NLP
Neural Networks + Text Vectors
  • ANN, CNN, RNN, LSTM, GRU
  • Activation, Dropout, Optimizers
  • Transfer Learning: ResNet
  • Word2Vec, GloVe, SBERT
Transformers & GenAI
LLMs & Prompt Engineering
  • Transformer Architecture, Attention
  • BERT, GPT, T5, BART
  • Tokenization, Prompting (Zero-shot, Few-shot)
  • Serving LLMs: OpenAI, Groq
  • LoRA, PEFT Overview
RAG & Agentic AI
RAG + Multi-Agent Frameworks
  • Chunking, Hybrid Retrieval (BM25 + FAISS)
  • Grounded Generation & Re-ranking
  • LangChain, CrewAI, LangGraph, AutoGen
  • Planner → Executor Loop, MCP

AI Engineer Interview Course vs Free Content

AI System Design & Case Studies

✔ Teaches end-to-end ML, GenAI, and agentic system design with scalability and latency in mind.
✘ Covers only basic concepts with no architectural planning or real-world design.

Model Optimization & Serving

✔ Includes quantization, LoRA/QLoRA fine-tuning, vLLM, DeepSpeed, and secure deployment.
✘ No content on inference optimization or scalable LLM deployment techniques.

ML/DL Fundamentals with Practical Depth

✔ Covers Linear/Logistic regression, SVM, Decision Trees, CNNs, RNNs, Transformers with projects.
✘ Superficial ML coverage without strong conceptual foundation or project work.

RAG, LLMOps & AgentOps

✔ Hands-on modules for Retrieval-Augmented Generation, agent systems, observability, and prompt drift.
✘ Misses enterprise GenAI stack; lacks depth on infra and evaluation.

Mock Interviews & System Debugging

✔ Real MLE/AI interviews, peer review feedback, and architecture debugging via expert guidance.
✘ No live guidance, peer review, or simulated interview practice.

Projects + GitHub Portfolio

✔ Interview-grade projects with LangChain, RAG, Vision, and vLLM. Tracked on GitHub.
✘ No versioned projects or interview-ready codebases provided.

Placement Prep & Career Coaching

✔ Focused prep for FAANG+ AI roles: referrals, DSA coaching, resume revamp, and mentorship.
✘ No career strategy, no technical interview roadmap, and zero referrals.

AI Interview Course Fees

Transparent pricing for every experience level. Learn, practice, and get placed — with no hidden fees.

Entry Level (0–2 yrs)

₹20,000

  • Python + DSA + ML Basics
  • Mock Interviews + Projects
  • Placement Support

Mid-Level (2–5 yrs)

₹25,000

  • RAG, GenAI, System Design
  • LLMOps, PromptOps Mastery
  • Interview Prep + Capstone

Leadership (5–8 yrs)

₹35,000

  • AI Infra, AgentOps, MCP
  • Project Governance + Strategy
  • Leadership Role Interviews
Enroll NowEnquire Now : +91 99978 00680

What Our Learners Say

Hear how learners cracked top AI roles with our interview prep

"I took this AI Engineering Interview course to prepare for ML roles at top companies. The modules on RAG, LLMOps, and System Design helped me clear 3 rounds at a product firm. Highly practical and industry-aligned!"
Harsha Reddy
AI Engineer (2–5 Yrs Experience)
"The DSA with Python and SQL refreshers were game-changers for me. Combined with mock interviews and deep learning modules, I cracked my first GenAI interview with confidence."
Yusuf Jafar
Machine Learning Engineer (0–2 Yrs)
"I had solid experience, but the AI System Design and Multi-Agent case studies helped me upgrade my thinking. The feedback on my capstone project was detailed and actionable."
Nitin Rajput
Applied Scientist (5+ Yrs)
"This course bridges the gap between ML knowledge and interview readiness. The LLMOps section, especially on vLLM and deployment, was exactly what I needed."
Om Prakash
Data Scientist (Mid-Level)
"From Day 1, this course felt like a personal coaching program. The neural networks, CNNs, and transformer breakdowns made even research-level prep feel accessible."
Sneha S
AI Research Associate
"The multi-modal topics, combined with case-based learning in AgentOps, gave me a clear edge. This course covers what real interviews demand, not just theory."
Ravi Krishnan
AI Engineer (Vision & LLM Stack)

Explore Other AI Interview Prep Tracks

ML Engineer Interview Prep

ML Interviews

System design, model optimization, and MLOps tailored for ML interviews.

Data Science Interview Prep

Data Interviews

Math, stats, SQL, EDA, and full interview prep for data roles.

Generative AI Interview Prep

GenAI Interviews

Master RAG, prompt engineering, agent workflows, and multimodal GenAI topics.

Your Questions Answered – AI Interview Course

Got More Questions?

Talk to Our Team Directly

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Enquire Now +91 9997800680 info@schoolofcoreai.com
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