AVASOFT is hiring Freshers candidates for AI/ML TRAINEE ENGINEERS. The details of the job, requirements and other information given below:
AVASOFT IS HIRING : AI/ML TRAINEE ENGINEERS
- Qualification : Any Bachelor’s Or master’s Degree
- 2022/2023/2024/2025 Batches can apply
- Strong grasp of relational database concepts and ER modeling.
- Proficient in writing efficient, scalable SQL queries aligned with business needs.
- Skilled in code debugging, analysis, and performance tuning.
- Solid understanding of AI concepts, including prompt engineering and RAG.
- Strong knowledge of ML pipelines: data preprocessing, model training, evaluation, and deployment.
- Location: India
Don’t miss out, CLICK HERE (to apply before the link expires)
AVASOFT – AI/ML Trainee Engineer Interview Questions & Answers
1. Tell me about yourself.
Answer:
My name is [Your Name]. I recently completed my [B.Tech/M.Tech] in [Computer Science/IT/Data Science] from [Your College]. I have a strong interest in Artificial Intelligence and Machine Learning because I enjoy solving real-world problems using data and code. During my studies, I worked on projects using Python, and I also learned about databases, data preprocessing, and model building. I’m now looking to start my career with a company like AVASOFT where I can learn from real projects and grow my skills in a professional setting.
2. Why do you want to join AVASOFT?
Answer:
AVASOFT is known for digital transformation and innovation. I’ve seen that the company works with advanced technologies like AI, ML, and cloud computing. I also noticed AVASOFT is a Microsoft Solutions Partner, which means I’ll get to work with industry-standard tools. As a fresher, I want to start my journey in a place where I’ll be challenged, trained, and guided. The projects at AVASOFT seem exciting, and I want to be part of a team that’s building real solutions for global clients.
3. What is your experience with SQL and database management?
Answer:
I have learned SQL during my coursework and used it in college projects. I understand how relational databases work — tables, primary keys, and foreign keys. I’ve written queries to select, update, delete, and insert data. I’ve also used functions like JOIN, GROUP BY, and WHERE to filter and combine data from different tables. I understand ER diagrams and how they help in designing databases. I’m confident I can write efficient and meaningful SQL queries for real business needs.
4. How would you explain your Python skills?
Answer:
I have been using Python for the past [X months/years]. I know the basic building blocks like data types, loops, functions, file handling, and exception handling. I’ve worked with libraries like NumPy and Pandas for data processing, and matplotlib for visualization. I’ve also done small projects like a loan prediction model and data scraping. I’m comfortable debugging Python code and can improve performance by using built-in methods. I also have some understanding of object-oriented programming and can write classes and reusable functions.
5. What is Machine Learning, and how does it work?
Answer:
Machine Learning is a part of AI where computers learn from data instead of being manually programmed. In simple words, it’s like teaching a machine how to solve problems by showing it examples. For example, if you give it data about house prices, it can learn to predict the price of a new house. The main steps are:
Collecting data
Preprocessing it (cleaning, handling missing values, etc.)
Training a model (using algorithms like decision trees or linear regression)
Evaluating the model (checking how well it performs)
Deploying the model so it can be used in real life.
6. Can you explain a Machine Learning pipeline?
Answer:
Yes. A machine learning pipeline is the complete process from raw data to a working model. It usually has these steps:
Data Collection – Getting the data from sources like files or databases
Data Preprocessing – Cleaning the data, converting text into numbers, removing missing values
Feature Engineering – Selecting the most useful data features
Model Training – Applying algorithms like Random Forest, Logistic Regression, or XGBoost
Evaluation – Using metrics like accuracy, precision, or recall to check performance
Deployment – Putting the model into an app or cloud so others can use it
This pipeline helps to keep the ML process organized and repeatable.
7. What are some AI concepts you know?
Answer:
I know the basics of how AI works — it’s about building smart systems that can solve problems, understand data, and make decisions. I’ve learned about Prompt Engineering, where you guide AI models like ChatGPT using specific instructions to get better results. I’ve also read about Retrieval-Augmented Generation (RAG), which is a way to make large language models give better answers by connecting them to external sources or documents. I’m also aware of things like image analysis, document intelligence, and web scraping, and I’m eager to learn more.
8. What is Prompt Engineering in AI?
Answer:
Prompt engineering is the process of designing and giving the right kind of instructions to a language model like ChatGPT so that it gives accurate and useful answers. For example, instead of just saying “Write a story,” if you say “Write a short story about a robot who saves a city,” the output will be more specific. It’s important because large language models understand better when the prompt is clear and detailed.
9. Have you deployed any AI/ML projects to the cloud?
Answer:
I have basic knowledge of cloud platforms like AWS and Azure. I’ve explored how to deploy models using services like AWS SageMaker and Azure Machine Learning. I know the basic steps — saving the model, creating an API using Flask or FastAPI, and deploying it using cloud infrastructure. I haven’t done full deployment yet, but I’m ready to learn and work with teams who can guide me through real-world deployments.
10. What is Object-Oriented Programming (OOP) in Python?
Answer:
Object-Oriented Programming is a way of writing code by creating “objects” that have data (called attributes) and functions (called methods). It helps make the code more organized and reusable. For example, if I’m building a car application, I can make a Car class with attributes like color and speed, and methods like accelerate() and brake(). In Python, we use class, __init__(), and self to write OOP code. It’s useful when working on large projects or real-time systems.
11. Have you done any projects using AI/ML? Explain one.
Answer:
Yes. I worked on a project to predict student performance based on attendance, marks, and activity data. First, I cleaned the dataset, handled missing values, and used label encoding for text values. Then I used the Random Forest algorithm for training. I split the data into training and testing sets and got an accuracy of around 85%. I also used matplotlib to plot the results. This project helped me understand real-world problems and how to apply machine learning step by step.
12. How would you debug an error in your Python code?
Answer:
When I see an error, I first read the error message carefully — Python usually tells you the line number and type of error. I then check that part of the code and see if I have a typo, a missing variable, or a logic issue. I sometimes use print() statements to check variable values or use debugging tools in an IDE like VS Code or Jupyter Notebook. I try small changes and test them step by step until the issue is fixed.
13. What are some areas you still want to improve or learn more about?
Answer:
I want to get more hands-on experience in cloud deployment using AWS or Azure. I also want to understand advanced topics like deep learning, model optimization, and AI ethics. I’ve read about things like model fine-tuning and pretraining, but I want to work on actual projects to understand them better. I believe AVASOFT will give me the chance to improve these areas.
14. Do you have any questions for us?
Answer:
Yes, I’d like to know:
What kind of training will I receive when I join as a trainee?
Will I work in a team, or will I have individual projects?
Are there opportunities to get a full-time role after the training period?
Final Tips for Candidates:
Revise Python and SQL basics, especially syntax and small hands-on tasks.
Practice explaining your college projects like you’re teaching a beginner.
Stay calm and answer slowly — it’s not about speed, it’s about clarity.
Use real examples or mini-projects to show your interest in AI/ML.
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