Skip to contentSkip to footer

Ai Developer Technology Engineer Interview Questions

Salaries
Interviews

3,129 ai developer technology engineer interview questions shared by candidates

AI Engineer interview questions cover a broad range of topics, including fundamental concepts like supervised vs. unsupervised learning, bias-variance tradeoff, and gradient descent, as well as practical skills in data preprocessing, feature engineering, model evaluation, and deployment. Interviewers often ask about specific algorithms (like CNNs, RNNs, LSTMs), deep learning frameworks (TensorFlow, PyTorch), and strategies for handling challenges such as imbalanced datasets and overfitting. Behavioral questions assess your project experience, problem-solving abilities, and how you stay updated in the field. Fundamental Concepts Types of Learning: Explain the differences between supervised, unsupervised, and reinforcement learning, and give examples. Bias-Variance Trade-off: Describe the concept of bias and variance in machine learning models and how they relate to model complexity and generalization. Overfitting & Underfitting: Define these concepts and the strategies you use to mitigate them. Activation Functions: Explain why activation functions are necessary in neural networks and name a few examples. Cost/Loss Functions: Describe the purpose of a loss function in the context of model training. Embeddings: Explain what embeddings are and how they are used to represent discrete data. Machine Learning & Deep Learning Algorithms Specific Architectures: Describe Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Long Short-Term Memory (LSTM) networks. Ensemble Methods: Explain concepts like bagging and boosting, and describe the Random Forest algorithm. Dimensionality Reduction: Explain techniques such as Principal Component Analysis (PCA). Transfer Learning: Explain how transfer learning is used to improve model performance, especially with limited data.
avatar

AI Engineer

Interviewed at Boston Consulting Group

4.2★
Oct 1, 2025

AI Engineer interview questions cover a broad range of topics, including fundamental concepts like supervised vs. unsupervised learning, bias-variance tradeoff, and gradient descent, as well as practical skills in data preprocessing, feature engineering, model evaluation, and deployment. Interviewers often ask about specific algorithms (like CNNs, RNNs, LSTMs), deep learning frameworks (TensorFlow, PyTorch), and strategies for handling challenges such as imbalanced datasets and overfitting. Behavioral questions assess your project experience, problem-solving abilities, and how you stay updated in the field. Fundamental Concepts Types of Learning: Explain the differences between supervised, unsupervised, and reinforcement learning, and give examples. Bias-Variance Trade-off: Describe the concept of bias and variance in machine learning models and how they relate to model complexity and generalization. Overfitting & Underfitting: Define these concepts and the strategies you use to mitigate them. Activation Functions: Explain why activation functions are necessary in neural networks and name a few examples. Cost/Loss Functions: Describe the purpose of a loss function in the context of model training. Embeddings: Explain what embeddings are and how they are used to represent discrete data. Machine Learning & Deep Learning Algorithms Specific Architectures: Describe Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Long Short-Term Memory (LSTM) networks. Ensemble Methods: Explain concepts like bagging and boosting, and describe the Random Forest algorithm. Dimensionality Reduction: Explain techniques such as Principal Component Analysis (PCA). Transfer Learning: Explain how transfer learning is used to improve model performance, especially with limited data.

Technical Questions Asked: -- TypeScript -Difference between union types and generic types. -How would you model an API response that can return either data or an error? -Interface vs Type in TypeScript and when to use each. -Preferred approach for type narrowing (discriminated unions vs property existence checks). -- Frontend / React -Local state vs global state management. -How to architect state management for a scalable enterprise React application. -Redux Toolkit, React.memo, useMemo, and state organization. -Deciding what belongs in the global store versus component state. --Performance -Core Web Vitals (LCP, INP, CLS). -Improving Largest Contentful Paint (LCP). -Cross-browser compatibility strategies. -Autoprefixer, polyfills, feature detection, and progressive enhancement. -Debugging browser-specific issues (especially Safari). -Debugging production-only JavaScript errors. --Architecture & Design -SOLID principles with practical examples. -Applying Liskov Substitution Principle and Interface Segregation Principle. -Designing maintainable microservices. -Improving architecture for tightly coupled applications. -Communication between microservices. -REST APIs vs asynchronous messaging. -API versioning and data consistency. --Java / Backend -Performance troubleshooting for enterprise Java applications. -Application-level metrics to monitor during performance analysis. -Database optimization, caching, and connection pooling. --Agile -Estimation techniques when requirements are ambiguous. -Story Points and Planning Poker. -Managing uncertainty and communicating risks to stakeholders. --Quality Assurance -Unit testing vs manual exploratory testing. -Importance of exploratory testing during release cycles. --Coding Round The coding exercise was based on Graphs. The problem required finding the number of Strongly Connected Components (SCC) in a directed graph. It involved implementing a graph traversal algorithm and handling directed edges correctly. If you're preparing for this interview, it's worth practicing: DFS Graph traversal Strongly Connected Components (Kosaraju's or Tarjan's Algorithm)
avatar

ReactJS Developer Interview (AI Screening)

Interviewed at Infosys

3.6★
Jun 26, 2026

Technical Questions Asked: -- TypeScript -Difference between union types and generic types. -How would you model an API response that can return either data or an error? -Interface vs Type in TypeScript and when to use each. -Preferred approach for type narrowing (discriminated unions vs property existence checks). -- Frontend / React -Local state vs global state management. -How to architect state management for a scalable enterprise React application. -Redux Toolkit, React.memo, useMemo, and state organization. -Deciding what belongs in the global store versus component state. --Performance -Core Web Vitals (LCP, INP, CLS). -Improving Largest Contentful Paint (LCP). -Cross-browser compatibility strategies. -Autoprefixer, polyfills, feature detection, and progressive enhancement. -Debugging browser-specific issues (especially Safari). -Debugging production-only JavaScript errors. --Architecture & Design -SOLID principles with practical examples. -Applying Liskov Substitution Principle and Interface Segregation Principle. -Designing maintainable microservices. -Improving architecture for tightly coupled applications. -Communication between microservices. -REST APIs vs asynchronous messaging. -API versioning and data consistency. --Java / Backend -Performance troubleshooting for enterprise Java applications. -Application-level metrics to monitor during performance analysis. -Database optimization, caching, and connection pooling. --Agile -Estimation techniques when requirements are ambiguous. -Story Points and Planning Poker. -Managing uncertainty and communicating risks to stakeholders. --Quality Assurance -Unit testing vs manual exploratory testing. -Importance of exploratory testing during release cycles. --Coding Round The coding exercise was based on Graphs. The problem required finding the number of Strongly Connected Components (SCC) in a directed graph. It involved implementing a graph traversal algorithm and handling directed edges correctly. If you're preparing for this interview, it's worth practicing: DFS Graph traversal Strongly Connected Components (Kosaraju's or Tarjan's Algorithm)

Question 1: Multimodal RAG Retrieval Question In a Retrieval-Augmented Generation (RAG) system, how would you efficiently handle and retrieve information from multiple types of datasets such as text, images, audio, video, and tables? For example: Text documents may contain paragraphs, structured tables, and columns Images may contain charts or diagrams Audio and video may contain spoken content How would you design the data processing and retrieval pipeline so that the system can retrieve the most relevant information efficiently when a user asks a question? Specifically explain: How each modality (text, tables, images, audio, video) would be processed How the data would be converted into embeddings How it would be stored in a vector database How the system would perform efficient retrieval across different data types Question 2: Resume Information Extraction Question You are given multiple resume templates, and each template contains different formats of date representations. Examples of date formats include: Jan 2022 – Mar 2023 2021 - Present 03/2020 – 07/2022 March 2019 to June 2021 Your task is to build a system that automatically extracts structured information from resumes, specifically: Project Name Project Duration Total Years of Experience Challenges: Resumes follow different templates and layouts Date formats are not consistent Information may appear in different sections
avatar

Generative AI Engineer

Interviewed at Infosys

3.6★
Mar 14, 2026

Question 1: Multimodal RAG Retrieval Question In a Retrieval-Augmented Generation (RAG) system, how would you efficiently handle and retrieve information from multiple types of datasets such as text, images, audio, video, and tables? For example: Text documents may contain paragraphs, structured tables, and columns Images may contain charts or diagrams Audio and video may contain spoken content How would you design the data processing and retrieval pipeline so that the system can retrieve the most relevant information efficiently when a user asks a question? Specifically explain: How each modality (text, tables, images, audio, video) would be processed How the data would be converted into embeddings How it would be stored in a vector database How the system would perform efficient retrieval across different data types Question 2: Resume Information Extraction Question You are given multiple resume templates, and each template contains different formats of date representations. Examples of date formats include: Jan 2022 – Mar 2023 2021 - Present 03/2020 – 07/2022 March 2019 to June 2021 Your task is to build a system that automatically extracts structured information from resumes, specifically: Project Name Project Duration Total Years of Experience Challenges: Resumes follow different templates and layouts Date formats are not consistent Information may appear in different sections

Viewing 1411 - 1420 interview questions

Glassdoor has 3,129 interview questions and reports from Ai developer technology engineer interviews. Prepare for your interview. Get hired. Love your job.