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AI Engineer Interview Prep

3 Experience Tracks Live Mock Interviews System Design Rounds Full GenAI Stack

Prepare for AI and GenAI engineering interviews with structured, role-specific training. This course covers DSA with Python, ML and DL fundamentals, System Design, LLMOps, RAG pipelines, and mock interview rounds so you walk into every interview with confidence.

Prerequisite: Basic Python and familiarity with ML concepts. Not sure if this is the right fit?Compare with our full AI course with career support.

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Not sure if interview prep is the right fit? Explore our full AI courses.

AI Course with Placement Generative AI Specialization

Why Join This AI Interview Prep Course

Every module is designed around a single goal: making you interview-ready for AI engineering roles.

Clear AI Engineering Interviews with Confidence

Practice real interview patterns for ML, GenAI, and system design rounds. Walk into every round prepared.

Practice with Live Mock Interview Rounds

Simulate actual hiring loops with expert feedback on technical depth, communication, and problem solving.

Master AI System Design from Scratch

Learn to design scalable ML pipelines, GenAI workflows, and agent architectures that interviewers look for.

Build an Interview-Ready Project Portfolio

Complete GitHub-tracked projects covering RAG pipelines, LLM deployment, and end-to-end AI systems.

Learn the Full GenAI and LLMOps Stack

Cover prompt engineering, retrieval-augmented generation, model serving, and production observability tools.

Get Resume and Application Strategy Support

Revamp your resume for AI roles, build outreach templates, and optimize your LinkedIn for recruiter visibility.

Strengthen DSA and Coding Fundamentals

Refresh Python data structures, algorithms, and SQL so you can handle coding rounds without hesitation.

Join a Community of Serious AI Practitioners

Study alongside engineers preparing for similar roles. Share resources, do peer reviews, and stay accountable.

Follow a Structured Track for Your Experience Level

Choose the right curriculum depth, whether you have 0 to 2, 2 to 5, or 5 to 8 years of experience.

Why Join Our AI Engineer Interview Course?

Crack Product AI Interviews with Confidence

Master AI interview patterns, frameworks, and deployment practices expected at leading product companies and high-growth startups.

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, 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.

How Mock Interviews Work

Our four-step process takes you from assessment to real interview confidence. Every session is designed to surface blind spots and sharpen your delivery.

1

Diagnostic Assessment

We evaluate your current strengths across DSA, ML fundamentals, system design, and GenAI. This sets your baseline and helps us customize your prep plan.

2

Weekly Practice Drills

Every week, you tackle timed problems and scenario questions that mirror real interview formats. Topics rotate across coding, modeling, and architecture.

3

Full Interview Loop Simulation

Experience a complete hiring loop with multiple rounds including coding, system design, ML deep dive, and behavioral. Conducted by experienced practitioners.

4

Detailed Scorecard and Feedback

After each simulation, receive a scorecard covering technical accuracy, communication clarity, problem breakdown, and areas to improve before the real thing.

Mock interview availability depends on your enrolled track. Sessions are scheduled after the first two weeks of coursework.

Skills You Will 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 Interview Preparation Curriculum

Choose your experience track and explore the full syllabus — from Python fundamentals through agentic AI system design.

1Python 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)
2Data Structures & Algorithms
Core DSA Patterns
  • Sorting: Bubble, Merge, Quick
  • Searching: Linear, Binary
  • Two Pointer & Sliding Window
  • Linked List: Reversal, Cycle Detection
3Databases for AI
SQL + NoSQL + Vector DBs
  • SQL Joins, Aggregation, Indexing
  • MongoDB Aggregation Framework
  • FAISS basics, similarity search
4API 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
5Machine 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
6Deep Learning & NLP
Neural Networks + Text Vectors
  • ANN, CNN, RNN, LSTM, GRU
  • Activation, Dropout, Optimizers
  • Transfer Learning: ResNet
  • Word2Vec, GloVe, SBERT
7Transformers & GenAI
LLMs & Prompt Engineering
  • Transformer Architecture, Attention
  • BERT, GPT, T5, BART
  • Tokenization, Prompting (Zero-shot, Few-shot)
  • Serving LLMs: OpenAI, Groq
  • LoRA, PEFT Overview
8RAG & 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.

Career Strategy and Application Support

✔ Resume revamp, outreach templates, application strategy, and ongoing mentorship for AI roles.
✘ No career strategy, no technical interview roadmap, and no structured support.

Industry-Trusted AI Interview Prep Certificate

After completing this AI Engineer Interview Prep Course, you will earn a recognized certificate that validates your readiness for AI engineering roles. It demonstrates your skills in system design, ML fundamentals, GenAI pipelines, and production-grade deployment to potential employers.

SCHOOL OF CORE AI

CERTIFICATE

OF ACHIEVEMENT

Has successfully completed the

AI Engineer Interview Preparation Course

and is competent in AI System Design, ML, GenAI & LLMOps

CERTIFIED
VERIFIED
ADVANCED

AI Interview Course Fees

Transparent pricing for every experience level. Learn, practice, and become interview-ready with no hidden fees.

0–2 yrs

Entry Level

₹20,000

  • Python + DSA + ML Basics
  • Mock Interviews + Projects
  • Career Support & Resume Review
Enroll Now
MOST POPULAR

2–5 yrs

Mid-Level

₹25,000

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

5–8 yrs

Leadership

₹35,000

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

Career support is not a job guarantee. Outcomes depend on learner effort, background, and market conditions. Prices are subject to change. GST additional where applicable.

What Our Learners Say

Hear how learners prepared for top AI roles with our interview prep

"The modules on RAG, LLMOps, and System Design gave me the structured prep I was missing. I felt confident walking into technical rounds at product companies."
ML
Mid-Level AI Engineer
2 to 5 Years Experience
"The DSA with Python refresher and mock interview sessions made a real difference. I went from nervous to prepared in about six weeks."
EC
Early-Career ML Engineer
0 to 2 Years Experience
"I had solid experience, but the AI System Design and multi-agent case studies helped me upgrade my thinking. The capstone feedback was detailed and actionable."
SA
Senior AI Practitioner
5 Plus Years Experience
"This course bridges the gap between ML knowledge and interview readiness. The LLMOps section on model serving and deployment was exactly what I needed."
DS
Data Scientist Transitioning to AI
Mid-Level
"From the first week, this felt like a personal coaching program. The deep learning and transformer breakdowns made even research-level prep feel accessible."
AR
AI Research Associate
Research Background
"The multimodal topics combined with case-based learning gave me a clear edge. This course covers what real interviews demand, not just theory."
VL
AI Engineer, Vision and LLM Focus
Specialized Track

Explore Other AI Interview Prep Tracks

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

Data Science Interview Prep

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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.

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.

Your Questions Answered - AI Interview Course

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Enquire Now +91 96914 40998 info@schoolofcoreai.com
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