AI Engineer Interview Path

Master AI engineering interviews with real-world use cases. Each scenario includes key topics, interview questions, and technical concepts you'll encounter at top tech companies.

Use Cases

40+

Interview Questions

Building a Customer Support Chatbot with LLMs

IntermediateLLM Integration

Design and implement an intelligent customer support chatbot using Large Language Models.

🎯 Key Topics to Master:

✓LLM API Integration (OpenAI, Anthropic)

✓Prompt Engineering & Context Management

✓Conversation State Management

✓Rate Limiting & Cost Optimization

✓Fallback Mechanisms & Error Handling

✓Response Streaming & Real-time Updates

💡 Common Interview Questions:

1.How would you handle context window limitations?
2.What strategies would you use to reduce LLM API costs?
3.How do you ensure consistent personality/tone across conversations?
4.How would you implement guardrails for harmful content?
5.What metrics would you track for chatbot quality?

🔧 Technical Concepts:

Token management and countingTemperature and sampling parametersSystem prompts vs user promptsFunction calling/tool useCaching strategies for repeated queries

📚

Implementing a RAG System for Document Q&A

AdvancedRAG Architecture

Build a Retrieval-Augmented Generation system to answer questions from large document collections.

🎯 Key Topics to Master:

✓Document Chunking Strategies

✓Vector Embeddings & Semantic Search

✓Vector Database Selection (Pinecone, Weaviate, ChromaDB)

✓Hybrid Search (Dense + Sparse)

✓Reranking Algorithms

✓Context Retrieval Optimization

💡 Common Interview Questions:

1.How do you decide optimal chunk size and overlap?
2.What are the tradeoffs between different vector databases?
3.How would you handle multi-modal documents (text + images)?
4.How do you evaluate RAG system quality?
5.What techniques improve retrieval accuracy?

🔧 Technical Concepts:

Embedding models (BERT, Sentence-BERT, OpenAI)Similarity metrics (cosine, euclidean, dot product)Indexing strategies (HNSW, IVF)Query rewriting and expansionAnswer synthesis from multiple chunks

🎯

Fine-tuning LLMs for Domain-Specific Tasks

AdvancedModel Training

Fine-tune pre-trained language models for specialized domains like legal, medical, or technical documentation.

🎯 Key Topics to Master:

✓Transfer Learning Fundamentals

✓Parameter-Efficient Fine-Tuning (PEFT)

✓LoRA and QLoRA Techniques

✓Training Data Preparation & Augmentation

✓Hyperparameter Tuning

✓Model Evaluation & Validation

💡 Common Interview Questions:

1.When should you fine-tune vs use prompt engineering?
2.How does LoRA reduce training costs?
3.What are the risks of catastrophic forgetting?
4.How do you handle data quality and bias?
5.What metrics indicate successful fine-tuning?

🔧 Technical Concepts:

Adapter layers and their benefitsGradient checkpointing for memory efficiencyLearning rate schedulingValidation set constructionDeployment considerations for fine-tuned models

🤝

Building Multi-Agent AI Systems

AdvancedAI Agents

Design systems where multiple AI agents collaborate to solve complex tasks.

🎯 Key Topics to Master:

✓Agent Architecture Design

✓Task Decomposition & Planning

✓Inter-Agent Communication Protocols

✓Tool/Function Calling

✓Memory & State Management

✓Agent Orchestration Frameworks (LangChain, AutoGen)

💡 Common Interview Questions:

1.How do agents coordinate and share information?
2.What are the tradeoffs of autonomous vs human-in-the-loop?
3.How do you handle agent disagreements or conflicts?
4.What safety mechanisms prevent infinite loops?
5.How do you debug multi-agent systems?

🔧 Technical Concepts:

ReAct (Reasoning + Acting) patternChain-of-thought promptingAgent tool selection and executionSupervisor agent patternsParallel vs sequential agent execution

😊

Real-time Sentiment Analysis Pipeline

IntermediateML Pipeline

Build a production-grade sentiment analysis system processing real-time data streams.

🎯 Key Topics to Master:

✓Streaming Data Processing (Kafka, Kinesis)

✓Model Serving & Inference Optimization

✓Batch vs Real-time Inference

✓Model Monitoring & Drift Detection

✓A/B Testing Infrastructure

✓Scalability & Load Balancing

💡 Common Interview Questions:

1.How do you optimize inference latency?
2.What are strategies for handling traffic spikes?
3.How do you detect and handle model drift?
4.What metrics track system health?
5.How do you version and rollback models?

🔧 Technical Concepts:

Model quantization and distillationBatch inference optimizationCaching prediction resultsFeature store architectureShadow mode deployment

🔍

AI-Powered Code Review Assistant

AdvancedDeveloper Tools

Create an AI system that automatically reviews code for bugs, style issues, and best practices.

🎯 Key Topics to Master:

✓Code Understanding & AST Parsing

✓Static Analysis Integration

✓LLM-based Code Review

✓Diff Analysis & Change Detection

✓GitHub/GitLab API Integration

✓Automated Comment Generation

💡 Common Interview Questions:

1.How do you balance automation with human oversight?
2.What techniques reduce false positives?
3.How do you handle different programming languages?
4.How do you prioritize which issues to flag?
5.What privacy concerns exist with code analysis?

🔧 Technical Concepts:

Code embeddings and similarity searchTree-sitter for language parsingPrompt design for code reviewIntegration with CI/CD pipelinesHistorical bug pattern learning

🎬

Personalized Content Recommendation Engine

AdvancedRecommendation Systems

Build a hybrid recommendation system combining collaborative filtering with LLM-based content understanding.

🎯 Key Topics to Master:

✓Collaborative Filtering Algorithms

✓Content-Based Filtering

✓Hybrid Recommendation Strategies

✓Cold Start Problem Solutions

✓Real-time Personalization

✓Diversity & Exploration vs Exploitation

💡 Common Interview Questions:

1.How do you handle new users with no history?
2.What are techniques to avoid filter bubbles?
3.How do you measure recommendation quality?
4.How do you incorporate real-time user feedback?
5.What are scalability challenges at millions of users?

🔧 Technical Concepts:

Matrix factorization techniquesNeural collaborative filteringTwo-tower model architectureMulti-armed bandit algorithmsApproximate nearest neighbor search

📊

AI Model Monitoring & Observability Platform

IntermediateMLOps

Design a comprehensive monitoring system for AI models in production.

🎯 Key Topics to Master:

✓Model Performance Metrics

✓Data Quality Monitoring

✓Concept Drift Detection

✓Latency & Throughput Tracking

✓Cost Monitoring & Optimization

✓Alerting & Incident Response

💡 Common Interview Questions:

1.What metrics indicate model degradation?
2.How do you detect data distribution shifts?
3.What triggers model retraining?
4.How do you balance cost and performance?
5.What visualization tools help diagnose issues?

🔧 Technical Concepts:

Statistical process controlKL divergence for distribution comparisonPrometheus & Grafana integrationFeature importance trackingShadow deployment for validation

📚 How to Use This Path

1. Study Each Use Case

Go through each scenario systematically. Understand the problem, architecture, and tradeoffs.

2. Practice Interview Questions

Prepare answers for each question. Practice explaining your thought process out loud.

3. Build Projects

Implement at least 2-3 use cases as portfolio projects. Document your decisions.

4. Deep Dive Technical Concepts

Master the technical concepts. Be ready to explain implementation details and alternatives.