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Genesys Ai Ml Engineer Interview Questions, Process, and Tips

Updated 19 Apr 2023

Genesys Ai Ml Engineer Interview Experiences

1 interview found

Interview experience
1
Bad
Difficulty level
Hard
Process Duration
Less than 2 weeks
Result
Not Selected

I was interviewed in Oct 2022.

Round 1 - Resume Shortlist 
Pro Tip by AmbitionBox:
Properly align and format text in your resume. A recruiter will have to spend more time reading poorly aligned text, leading to high chances of rejection.
View all tips
Round 2 - Technical 

(5 Questions)

  • Q1. Questions from the resume
  • Q2. Working of the mobilenet architecture
  • Ans. 

    MobileNet is a lightweight deep learning architecture designed for mobile and embedded vision applications.

    • MobileNet uses depthwise separable convolutions to reduce the number of parameters and computations.

    • It has a small memory footprint and can be easily deployed on mobile devices.

    • MobileNet has several variants, including MobileNetV1, MobileNetV2, and MobileNetV3.

    • MobileNetV2 introduced linear bottlenecks and inverted...

  • Answered by AI
  • Q3. What is so special in mobilenet
  • Ans. 

    MobileNet is a lightweight deep learning model designed for mobile and embedded devices.

    • MobileNet uses depthwise separable convolutions to reduce the number of parameters and computations.

    • It has a small memory footprint and can be easily deployed on mobile and embedded devices.

    • MobileNet has been used for various applications such as image classification, object detection, and semantic segmentation.

    • It has achieved state...

  • Answered by AI
  • Q4. Why does it consume less computing power in comparision to other DL architectures
  • Ans. 

    DL architectures like CNNs require more computing power due to their complex structure and operations.

    • The architecture of Ai Ml is simpler compared to other DL architectures like CNNs.

    • It uses a single layer of neurons which reduces the number of computations required.

    • It also uses a linear activation function which is computationally less expensive.

    • Ai Ml is suitable for simpler tasks like linear regression and classific...

  • Answered by AI
  • Q5. Mobilenet architecture vs other DL architecture differences
  • Ans. 

    Mobilenet is a lightweight DL architecture designed for mobile devices.

    • Mobilenet uses depthwise separable convolutions to reduce computation and model size.

    • It has fewer parameters and lower computational requirements compared to other DL architectures like VGG and ResNet.

    • Mobilenet is optimized for mobile devices and can run in real-time on smartphones and other embedded devices.

    • Other DL architectures like VGG and ResNe...

  • Answered by AI
Round 3 - Coding Test 

Implement self-attention from scratch

Interview Preparation Tips

Interview preparation tips for other job seekers - The coding test was completely a bad experience and had a negative impact. The interviewer asked me to implement the self-attention mechanism from scratch in an one hour interview for coding round that even the experienced NLP Engineers would find it difficult to implement! The interviewer was rude and ended the interview in a rude and very unprofessional manner. I left the interview completely demoralized and disrespected.

Skills evaluated in this interview

Interview questions from similar companies

Round 1 - Resume Shortlist 
Pro Tip by AmbitionBox:
Keep your resume crisp and to the point. A recruiter looks at your resume for an average of 6 seconds, make sure to leave the best impression.
View all tips
Round 2 - One-on-one 

(2 Questions)

  • Q1. Question on NLP and Coding test
  • Q2. Maximum coding question from list and Regex
Round 3 - One-on-one 

(1 Question)

  • Q1. Project based question

Interview Preparation Tips

Interview preparation tips for other job seekers - Please prepare for Python coding question and Be prepare with your project
Interview experience
4
Good
Difficulty level
Hard
Process Duration
Less than 2 weeks
Result
No response

I applied via Job Portal and was interviewed in Dec 2024. There was 1 interview round.

Round 1 - Technical 

(1 Question)

  • Q1. Question on random forest and linear regression
Interview experience
4
Good
Difficulty level
Easy
Process Duration
Less than 2 weeks
Result
Selected Selected

I applied via Approached by Company and was interviewed in Jul 2024. There were 2 interview rounds.

Round 1 - One-on-one 

(4 Questions)

  • Q1. Explain the ML project you recently worked?
  • Ans. 

    Developed a recommendation system for an e-commerce platform using collaborative filtering

    • Used collaborative filtering to analyze user behavior and recommend products

    • Implemented matrix factorization techniques to improve recommendation accuracy

    • Evaluated model performance using metrics like RMSE and precision-recall curves

  • Answered by AI
  • Q2. Questions related to ML fundamentals like supervised learning, unsupervised learning, evaluation and ML algorithms
  • Q3. Project specific questions
  • Q4. Easy-medium coding questions
Round 2 - HR 

(2 Questions)

  • Q1. What technologies you are working on?
  • Ans. 

    I am currently working on developing machine learning models using Python, TensorFlow, and scikit-learn.

    • Python programming language

    • TensorFlow framework

    • scikit-learn library

  • Answered by AI
  • Q2. How you will approach on machine learning problem?
  • Ans. 

    I would approach a machine learning problem by first understanding the problem, collecting and preprocessing data, selecting a suitable algorithm, training the model, evaluating its performance, and fine-tuning it.

    • Understand the problem statement and define the objectives clearly.

    • Collect and preprocess the data to make it suitable for training.

    • Select a suitable machine learning algorithm based on the problem type (clas...

  • Answered by AI

Interview Preparation Tips

Interview preparation tips for other job seekers - Focus on your skill and project which you worked on

Skills evaluated in this interview

Interview experience
3
Average
Difficulty level
Moderate
Process Duration
-
Result
No response

I was interviewed in Oct 2024.

Round 1 - Technical 

(3 Questions)

  • Q1. What is OHE ( one hot encoding)
  • Ans. 

    OHE is a technique used in machine learning to convert categorical data into a binary format.

    • OHE is used to convert categorical variables into a format that can be provided to ML algorithms.

    • Each category is represented by a binary vector where only one element is 'hot' (1) and the rest are 'cold' (0).

    • For example, if we have a 'color' feature with categories 'red', 'blue', 'green', OHE would represent them as [1, 0, 0],

  • Answered by AI
  • Q2. What is conditional probability
  • Ans. 

    Conditional probability is the likelihood of an event occurring given that another event has already occurred.

    • Conditional probability is calculated using the formula P(A|B) = P(A and B) / P(B)

    • It represents the probability of event A happening, given that event B has already occurred

    • Conditional probability is used in various fields such as machine learning, statistics, and finance

  • Answered by AI
  • Q3. What is precision, recall
  • Ans. 

    Precision and recall are evaluation metrics used in machine learning to measure the performance of a classification model.

    • Precision is the ratio of correctly predicted positive observations to the total predicted positive observations.

    • Recall is the ratio of correctly predicted positive observations to the all observations in actual class.

    • Precision is important when the cost of false positives is high, while recall is i...

  • Answered by AI

Skills evaluated in this interview

Interview experience
4
Good
Difficulty level
Moderate
Process Duration
Less than 2 weeks
Result
Not Selected

I applied via Naukri.com and was interviewed in Jul 2024. There was 1 interview round.

Round 1 - Technical 

(5 Questions)

  • Q1. Describe any NLP project end to end.
  • Ans. 

    Developed a sentiment analysis model using NLP to analyze customer reviews for a product.

    • Collected and preprocessed text data from various sources

    • Performed tokenization, stopword removal, and lemmatization

    • Built a machine learning model using techniques like TF-IDF and LSTM

    • Evaluated the model's performance using metrics like accuracy and F1 score

    • Deployed the model for real-time sentiment analysis of new reviews

  • Answered by AI
  • Q2. What is cosine similarity?
  • Ans. 

    Cosine similarity is a measure of similarity between two non-zero vectors in an inner product space.

    • It measures the cosine of the angle between the two vectors.

    • Values range from -1 (completely opposite) to 1 (exactly the same).

    • Used in recommendation systems, text mining, and clustering algorithms.

  • Answered by AI
  • Q3. What is difference between iterator and iterable?
  • Ans. 

    Iterator is an object that allows iteration over a collection, while iterable is an object that can be iterated over.

    • Iterator is an object with a next() method that returns the next item in the collection.

    • Iterable is an object that has an __iter__() method which returns an iterator.

    • Example: List is iterable, while iter(list) returns an iterator.

  • Answered by AI
  • Q4. Write a python function for cosine similarity.
  • Ans. 

    Python function to calculate cosine similarity between two vectors.

    • Define a function that takes two vectors as input.

    • Calculate the dot product of the two vectors.

    • Calculate the magnitude of each vector and multiply them.

    • Divide the dot product by the product of magnitudes to get cosine similarity.

  • Answered by AI
  • Q5. How did you evaluate your model. what is F1 score.
  • Ans. 

    F1 score is a metric used to evaluate the performance of a classification model by considering both precision and recall.

    • F1 score is the harmonic mean of precision and recall, calculated as 2 * (precision * recall) / (precision + recall).

    • It is a better metric than accuracy when dealing with imbalanced datasets.

    • A high F1 score indicates a model with both high precision and high recall.

    • F1 score ranges from 0 to 1, where

  • Answered by AI

Skills evaluated in this interview

Interview experience
5
Excellent
Difficulty level
Moderate
Process Duration
Less than 2 weeks
Result
Selected Selected

I applied via Company Website and was interviewed in Jul 2023. There were 4 interview rounds.

Round 1 - Resume Shortlist 
Pro Tip by AmbitionBox:
Keep your resume crisp and to the point. A recruiter looks at your resume for an average of 6 seconds, make sure to leave the best impression.
View all tips
Round 2 - HR 

(1 Question)

  • Q1. Normal HR round that happens as the initial screening process
Round 3 - Coding Test 

Python basic coding questions (pandas, numpy, OOPs concept.
AI ML theory questions

Round 4 - HR 

(1 Question)

  • Q1. Normal salary discussion
Interview experience
4
Good
Difficulty level
Easy
Process Duration
Less than 2 weeks
Result
Selected Selected

I applied via Referral and was interviewed in Jul 2024. There were 3 interview rounds.

Round 1 - Assignment 

30 MCQs where 15 Need to be answered correctly to get shortlisted.
Sanfoundary source is very helpful in cracking it.

Round 2 - Technical 

(4 Questions)

  • Q1. Explain Oops concepts
  • Ans. 

    Oops concepts refer to Object-Oriented Programming principles such as Inheritance, Encapsulation, Polymorphism, and Abstraction.

    • Inheritance: Allows a class to inherit properties and behavior from another class.

    • Encapsulation: Bundling data and methods that operate on the data into a single unit.

    • Polymorphism: Ability to present the same interface for different data types.

    • Abstraction: Hiding the complex implementation det

  • Answered by AI
  • Q2. Explain file handling
  • Ans. 

    File handling refers to the process of managing and manipulating files on a computer system.

    • File handling involves tasks such as creating, reading, writing, updating, and deleting files.

    • Common file operations include opening a file, reading its contents, writing data to it, and closing the file.

    • File handling in programming languages often involves using functions or libraries specifically designed for file operations.

    • E...

  • Answered by AI
  • Q3. Explain supervised and unsupervised learning algorithms of your choice.
  • Ans. 

    Supervised learning uses labeled data to train a model, while unsupervised learning finds patterns in unlabeled data.

    • Supervised learning requires input-output pairs for training

    • Examples include linear regression, support vector machines, and neural networks

    • Unsupervised learning clusters data based on similarities or patterns

    • Examples include k-means clustering, hierarchical clustering, and principal component analysis

  • Answered by AI
  • Q4. Coding question on pandas which had 10 followup questions
Round 3 - HR 

(1 Question)

  • Q1. Simple discussion on compensation

Interview Preparation Tips

Interview preparation tips for other job seekers - Easy to moderate interview. Stay focused on basics.

Skills evaluated in this interview

Interview experience
3
Average
Difficulty level
-
Process Duration
-
Result
-
Round 1 - One-on-one 

(2 Questions)

  • Q1. Ask me on project
  • Q2. Aws, sql, python, flask ask me

Interview Preparation Tips

Interview preparation tips for other job seekers - Not selected
Interview experience
4
Good
Difficulty level
-
Process Duration
-
Result
-
Round 1 - One-on-one 

(2 Questions)

  • Q1. What is zip function
  • Ans. 

    The zip function in Python is used to combine multiple iterables into a single iterable of tuples.

    • Zip function takes two or more iterables as arguments and returns an iterator of tuples where the i-th tuple contains the i-th element from each of the input iterables.

    • If the input iterables are of different lengths, the resulting iterator will only have as many elements as the shortest input iterable.

    • Example: zip([1, 2, 3...

  • Answered by AI
  • Q2. What is NLP in Machine learning
  • Ans. 

    NLP (Natural Language Processing) in machine learning is the ability of a computer to understand, interpret, and generate human language.

    • NLP enables machines to analyze and derive meaning from human language data.

    • It involves tasks such as text classification, sentiment analysis, named entity recognition, and machine translation.

    • Examples of NLP applications include chatbots, language translation services, and speech rec

  • Answered by AI

Skills evaluated in this interview

Genesys Interview FAQs

How many rounds are there in Genesys Ai Ml Engineer interview?
Genesys interview process usually has 3 rounds. The most common rounds in the Genesys interview process are Technical, Coding Test and Resume Shortlist.
What are the top questions asked in Genesys Ai Ml Engineer interview?

Some of the top questions asked at the Genesys Ai Ml Engineer interview -

  1. Why does it consume less computing power in comparision to other DL architectur...read more
  2. What is so special in mobile...read more
  3. Mobilenet architecture vs other DL architecture differen...read more

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Genesys Ai Ml Engineer Interview Process

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