AI/ML Engineer

Description

Auto-generated position for AI/ML Engineer

Interview Questions (147)

Q1What are the key differences between supervised and unsupervised learning?
Technical
Q2Describe a challenging project you worked on in machine learning and how you overcame the obstacles.
Behavioral
Q3What specific web scraping tools or libraries have you used in your projects, and what were their advantages?
Experience
Q4If you were given a dataset with missing values, what steps would you take to handle it?
Situational
Q5What motivates you to work in the field of artificial intelligence and machine learning?
Motivational
Q6How do you stay updated with the latest developments in AI and machine learning?
Behavioral
Q7Can you describe your experience with implementing retrieval-augmented generation (RAG) models?
Experience
Q8What are the key considerations when designing a computer vision model for real-time processing?
Technical
Q9If a model you deployed is underperforming, what steps would you take to troubleshoot and improve it?
Situational
Q10What questions do you have about our team's approach to machine learning projects?
Reverse
Q11Describe a time when you faced a significant challenge in a project. How did you overcome it?
Behavioral
Q12If you were tasked with improving the accuracy of a computer vision model, what steps would you take?
Situational
Q13How do you stay current with the latest developments in machine learning and AI?
Experience
Q14Can you discuss your experience with implementing retrieval-augmented generation (RAG) models?
Technical
Q15Tell me about a time when you had to work closely with a team to achieve a project goal. How did you contribute?
Behavioral
Q16What are the most important metrics you consider when evaluating the performance of a machine learning model?
Technical
Q17How do you approach debugging in a machine learning model?
Technical
Q18Tell me about a time when you encountered a conflict while working on a team project. How did you handle it?
Behavioral
Q19Explain how you have utilized web scraping in your previous projects and the challenges you encountered.
Experience
Q20What machine learning frameworks are you most familiar with, and why do you prefer them?
Technical
Q21How do you stay updated with the latest advancements in machine learning technologies?
Motivational
Q22What would you do if you discovered that a deployed model was underperforming in production?
Situational
Q23If a client requested a computer vision solution for a problem you are unfamiliar with, how would you approach the project?
Situational
Q24What motivates you to work in machine learning, and what are your career goals in this field?
Motivational
Q25Can you describe a web scraping project you have worked on and the challenges you faced during its implementation?
Experience
Q26How would you approach improving the performance of a RAG-based model you're currently using?
Technical
Q27If you were tasked with developing a new computer vision model for a novel application, what steps would you take to ensure its success?
Situational
Q28Can you describe a specific data scraping project you worked on and the technologies you used?
Experience
Q29How would you approach improving the accuracy of a deep learning model that is underperforming?
Technical
Q30If you were leading a project with a tight timeline and limited resources, how would you prioritize tasks?
Situational
Q31Tell me about a time when you had to work with a difficult team member.
Behavioral
Q32What specific AI/ML project have you worked on that you are most proud of, and what was your role in it?
Experience
Q33What excites you the most about working in artificial intelligence?
Motivational
Q34Describe a machine learning project you worked on that had a significant impact on your team or organization.
Experience
Q35How would you approach a situation where your machine learning model is underperforming?
Situational
Q36Can you explain the steps you would take to preprocess a dataset for training a machine learning model?
Technical
Q37Describe a challenging project you worked on in the AI/ML field and how you overcame any obstacles you faced.
Experience
Q38Describe a situation where you had to collaborate with a team member who disagreed with your approach. How did you handle it?
Behavioral
Q39If you were tasked with integrating a retrieval-augmented generation (RAG) model into an existing application, what steps would you take?
Situational
Q40What excites you the most about working in the field of AI and machine learning, and how does this role align with your career aspirations?
Motivational
Q41Can you explain the process you followed to implement an RAG-based model in one of your projects?
Experience
Q42Describe a time when you faced a significant challenge in a web scraping project and how you overcame it.
Behavioral
Q43Can you describe a challenging machine learning project you worked on and what your specific contributions were?
Experience
Q44How do you prioritize tasks when working on multiple AI projects simultaneously?
Behavioral
Q45Can you describe a machine learning project you have worked on, including the challenges you faced and how you overcame them?
Experience
Q46How would you handle a situation where your team disagrees on the approach to a machine learning problem?
Behavioral
Q47Can you describe a challenging problem you faced while developing a machine learning model and how you resolved it?
Experience
Q48Can you describe a specific project where you applied machine learning to solve a real-world problem, and what was your role in it?
Experience
Q49How do you handle situations where your project doesn't go as planned, and can you give an example from your past work?
Behavioral
Q50Describe a challenging project you worked on and how you overcame the obstacles you faced during its development.
Behavioral
Q51Can you describe the architecture and technologies you used to build the AI-powered contract chatbot at ContractSPAN?
Technical
Q52What are foundation models, and how have they changed AI engineering?
Technical
Q53What is a Large Language Model (LLM), and how does it work?
Technical
Q54What is the Transformer architecture and how does it work?
Technical
Q55What is tokenization in LLMs?
Technical
Q56What is self-attention, and how does it work in Transformers?
Technical
Q57What is Cross Attention in Transformers?
Technical
Q58What is causal masking?
Technical
Q59What are multi-head attention mechanisms? Why use multiple attention heads?
Technical
Q60What is Generative AI?
Technical
Q61What is the context window in LLMs, and why does it matter?
Technical
Q62What is temperature in the context of LLMs, and how does it affect output?
Technical
Q63What is the difference between open-source and closed-source LLMs? When would you choose one over the other?
Technical
Q64What is the difference between encoder-only, decoder-only, and encoder-decoder Transformer architectures?
Technical
Q65What is model distillation, and how is it used with LLMs?
Technical
Q66What is fine-tuning, and when should you fine-tune an LLM?
Technical
Q67What is the difference between full fine-tuning and parameter-efficient fine-tuning (PEFT)?
Technical
Q68What is LoRA (Low-Rank Adaptation), and how does it work?
Technical
Q69What is QLoRA, and how does it enable fine-tuning on consumer hardware?
Technical
Q70What is the role of metadata in vector databases?
Technical
Q71What is a vector database, and how does it differ from a traditional database?
Technical
Q72What is Retrieval-Augmented Generation (RAG), and why is it important?
Technical
Q73What is prompt engineering, and why is it critical for AI applications?
Technical
Q74What is the difference between autoregressive and masked language modeling?
Technical
Q75What is the difference between structured output and function calling?
Technical
Q76What is the difference between single-agent and multi-agent systems?
Technical
Q77What is the human-in-the-loop pattern for agents, and when is it needed?
Technical
Q78What is agent orchestration, and how do you implement it?
Technical
Q79What is the difference between reactive and proactive agents?
Technical
Q80How do you evaluate and test AI agents?
Technical
Q81What is the role of feature flags in AI deployments?
Technical
Q82How do you implement logging and tracing for LLM applications?
Technical
Q83How do you handle PII and sensitive data in LLM inputs and outputs?
Technical
Q84How do you implement content filtering for AI outputs?
Technical
Q85How do you handle model updates and migrations without downtime?
Technical
Q86How do you implement A/B testing for LLM systems?
Technical
Q87How do you design for latency vs quality trade-offs in AI systems?
Technical
Q88How do you manage secrets and API keys securely in LLM applications?
Technical
Q89How do you implement structured output from LLMs reliably in production?
Technical
Q90How do you handle long contexts efficiently in production (context compression, prefix caching)?
Technical
Q91How do you implement rate limiting and throttling for LLM APIs?
Technical
Q92How do you design an AI system for high availability and fault tolerance?
Technical
Q93How do you handle failover and fallback strategies for AI systems?
Technical
Q94How do you implement fallback strategies when the primary model is unavailable or rate-limited?
Technical
Q95How do you design an AI system that gracefully degrades when the model is unavailable?
Technical
Q96How do you approach capacity planning for an AI system?
Technical
Q97How do you evaluate LLM outputs? What metrics do you use?
Technical
Q98What is evaluation-driven development for AI applications?
Technical
Q99What is adversarial testing for AI systems?
Technical
Q100How do you build a regression test suite for AI applications?
Technical
Q101What is red teaming, and how do you red team an LLM application?
Technical
Q102How do you detect and measure hallucinations in LLM outputs?
Technical
Q103What is G-Eval, and how does it use LLMs for evaluation?
Technical
Q104What is LLM-as-a-judge evaluation, and what are its limitations?
Technical
Q105What are benchmark suites for evaluating AI systems?
Technical
Q106What's the trade-off between bias and variance?
Technical
Q107Explain over- and under-fitting and how to combat them?
Technical
Q108What is regularization, why do we use it, and give some examples of common methods?
Technical
Q109Can you explain the differences between supervised, unsupervised, and reinforcement learning?
Technical
Q110What’s the difference between a generative and discriminative model?
Technical
Q111Why do ensembles typically have higher scores than individual models?
Technical
Q112What's the difference between boosting and bagging?
Technical
Q113What is data normalization and why do we need it?
Technical
Q114What is an imbalanced dataset? Can you list some ways to deal with it?
Technical
Q115What is data augmentation? Can you give some examples?
Technical
Q116What is gradient descent?
Technical
Q117What is cost function?
Technical
Q118Define Learning Rate.
Technical
Q119What is Momentum (w.r.t NN optimization)?
Technical
Q120What is the difference between Batch Gradient Descent and Stochastic Gradient Descent?
Technical
Q121How does a neural network work?
Technical
Q122Why is ReLU better and more often used than Sigmoid in Neural Networks?
Technical
Q123What is vanishing gradient?
Technical
Q124What are dropouts?
Technical
Q125What is batch normalization and why does it work?
Technical
Q126What is the significance of Residual Networks?
Technical
Q127Define LSTM.
Technical
Q128What is the basic difference between LSTM and Transformers?
Technical
Q129What is Autoencoder, name few applications.
Technical
Q130Describe how convolution works. What about if your inputs are grayscale vs RGB imagery? What determines the shape of the next layer?
Technical
Q131Why do we use convolutions for images rather than just FC layers?
Technical
Q132Why do segmentation CNNs typically have an encoder-decoder style / structure?
Technical
Q133How do you combat the curse of dimensionality?
Technical
Q134Explain Principal Component Analysis (PCA)?
Technical
Q135What is the difference between LDA and PCA for dimensionality reduction?
Technical
Q136What is t-SNE?
Technical
Q137What is the difference between t-SNE and PCA for dimensionality reduction?
Technical
Q138What is UMAP?
Technical
Q139What is the difference between t-SNE and UMAP for dimensionality reduction?
Technical
Q140Why do we need a validation set and test set? What is the difference between them?
Technical
Q141What is stratified cross-validation and when should we use it?
Technical
Q142What is Precision?
Technical
Q143What is Recall?
Technical
Q144Define F1-score.
Technical
Q145Explain how a ROC curve works.
Technical
Q146How Random Number Generator Works, e.g. rand() function in python works?
Technical
Q147What is the difference between Bayesian vs frequentist statistics?
Technical