Vishal’s Journey to Becoming a Senior Machine Learning Engineer

The journey from being a Computer Science graduate to building a successful career in Machine Learning can be challenging, especially for freshers entering a competitive technology industry. However, with the right training, practical exposure, and continuous learning, it is possible to progress steadily toward professional opportunities. Vishal’s journey is a great example of how focused learning and consistent skill development can support career growth in the AI and Machine Learning field.

A 2019 Computer Science and Engineering graduate from Rajasthan Technical University, Vishal joined DataMites to develop his knowledge of Python, Data Science, and Machine Learning. During his learning journey, he gained practical experience through client projects and later worked as a Data Scientist across education and manufacturing domains. Today, he works as a Senior Machine Learning Engineer at Pontify. He shares his career journey, interview preparation experiences, essential skills for freshers, resume lessons, and valuable advice for aspiring Machine Learning professionals.

How Vishal Transitioned from Computer Science to Machine Learning and AI

Vishal’s journey from a Computer Science graduate to a Senior Machine Learning Engineer highlights the importance of continuous learning and practical experience. His transition into Machine Learning and Artificial Intelligence reflects his commitment to building skills in the evolving technology industry.

1. Can you tell us about yourself and your educational background?

My name is Vishal Solanki, and I am currently working as a Senior Machine Learning Engineer at Quantiphi. I am from Jodhpur, Rajasthan, and have been living in Bangalore. I graduated in Computer Science and Engineering from Rajasthan Technical University in 2019. Currently, I am also pursuing my master’s from IIT Patna.

2. Why did you choose data science and AI after studying Computer Science?

During the pandemic, one of my friends told me that data science was becoming popular in the IT industry. I researched the field, understood what data science involved, became interested, and decided to pursue a course in this area.

3. Did you know Python before joining the data science course?

No, I did not know Python before joining the course. I learned Python after starting my training. Initially, I did not have much interest in working in this field, but gradually I developed an interest and continued learning.

4. How did your Computer Science background help you in AI and data science?

My Computer Science background gave me a basic technical understanding, but I learned many data science concepts separately. During the course, I developed my knowledge of Python, machine learning, data science, and other skills required for working with data.

5. What did you learn during your DataMites training?

During the course, I learned Python, data science, and machine learning concepts. I also got an opportunity to work on client projects, which helped me understand how the concepts I learned could actually be applied to practical business problems.

6. How did your career progress after completing your training?

After completing my training, I joined an organization as a data scientist and worked on education-related projects using NLP and machine learning. Later, I joined Vimerra Systems and worked on manufacturing projects using deep learning, machine learning, and Python.

7. What does a Machine Learning Engineer do on a daily basis?

First, we understand the business problem, what data we have, and what our actual goal is. Then we prepare the data accordingly, perform EDA, and follow different steps to develop a suitable machine learning solution for the business problem.

8. Why is Exploratory Data Analysis important in machine learning projects?

EDA is actually used in projects. We need to understand the data properly before moving toward modeling. We look at the data, understand relationships and patterns, and prepare it properly according to the business objective.

9. Are Power BI and Tableau useful for AI professionals?

Yes, I have used Power BI and have basic knowledge of Tableau for visualization. It depends on the project. Sometimes these tools help us understand data and relationships between the target variable and other variables.

10. What should freshers include in a Machine Learning resume?

I would suggest keeping only the skills you genuinely know. If you mention a technology on your resume, interviewers can ask questions about it. So, it is better to have fewer skills that you understand properly.

11. What kind of Python coding questions can be asked in interviews?

They can ask simple questions such as writing a program to reverse a string, working with lists, adding or merging elements, or using loops. The main purpose is to check whether your Python basics and logical thinking are clear.

12. How important is logical thinking in Machine Learning interviews?

Logical thinking is very important. Interviewers may give questions to understand how you approach a problem and whether you can think logically. It is not always about memorizing concepts; they also want to understand your problem-solving approach.

13. What questions are commonly asked about machine learning projects?

Interviewers usually ask about the projects you have worked on. You should know the data, techniques, algorithms, and concepts you used. If you have worked on a project, you should understand everything you have actually implemented.

14. How should candidates prepare for modern AI technologies such as Generative AI?

You can learn Generative AI and other new technologies, but your fundamentals should be strong first. Nowadays, people are learning newer AI technologies, but understanding the concepts behind these technologies is also very important.

15. Is it a good idea to rely completely on ChatGPT for coding?

No, we should use ChatGPT as a helper, not depend completely on it. If the generated code has an error, we should try to understand and debug it ourselves instead of repeatedly asking AI for another answer.

16. What do you think about the future of AI and Machine Learning?

Currently, Agentic AI and similar technologies are trending, but I cannot say exactly what will happen in the next five years. Technology changes very quickly, so we should keep learning and stay prepared for new developments.

Refer to these articles:

• Supervised vs. Unsupervised Learning: What’s the Difference?
• Generative AI and Predictive AI: Key Differences Explained
• Guide to Artificial Intelligence Career

Vishal’s Key Takeaways on Building a Career in Machine Learning and AI

Vishal’s journey shows how strong fundamentals, practical experience, and continuous learning can help professionals build a successful career in Machine Learning and Artificial Intelligence. As the demand for IT courses continues to grow among graduates seeking technology careers, his experience offers useful guidance for learners choosing the right skills and career path.

• Background: Vishal completed his Computer Science and Engineering degree in 2019 from Rajasthan Technical University.
• Career Journey: He progressed from Data Scientist roles to becoming a Senior Machine Learning Engineer.
• Early Skills: He developed Python, Data Science, and Machine Learning skills through DataMites training.
• Practical Experience: He gained hands-on exposure by working on client projects during his learning journey.
• Project Experience: He worked on education-related projects using NLP and Machine Learning.
• Industry Exposure: At Vimerra Systems, he worked on manufacturing projects using Machine Learning, Deep Learning, and Python.
• Technical Tools: He gained experience with NumPy, Pandas, Scikit-learn, Matplotlib, Seaborn, Jupyter Notebook, and VS Code.
• Visualization: He used Power BI and developed basic knowledge of Tableau for visualization.
• Resume Advice: He recommends mentioning only skills that candidates genuinely understand and can confidently explain.
• Interview Preparation: He emphasizes Python fundamentals, logical thinking, project knowledge, mathematics, and statistics.
• AI Learning: He recommends building strong fundamentals while exploring emerging technologies such as Generative AI and Agentic AI.
• Career Advice: He encourages aspiring professionals to strengthen their basics, learn from interview experiences, and continuously improve their skills.

Refer to these articles:

• How Safeer Landed His First AI Engineer Job as a Fresher
• How Kanchi Built Her Career in Artificial Intelligence and Automation
• Surender's Journey to Becoming a Senior Generative AI Manager

Vishal’s journey shows how strong fundamentals, continuous learning, and practical experience can help professionals progress in Machine Learning and Artificial Intelligence. He built his foundation in Computer Science, developed skills in Python, Data Science, and Machine Learning, and gained real-world project experience. His journey highlights the value of practical learning for those exploring an Artificial Intelligence Course in Jaipur.

DataMites Institute provides industry-focused Artificial Intelligence Courses. The 9-month program includes approximately 780 hours of structured learning, covering Python, Machine Learning, Deep Learning, Data Analytics, Generative AI, and other relevant AI technologies. With practical projects, mentor guidance, and placement assistance, learners gain hands-on experience while developing job-ready skills. The program also provides opportunities to earn IABAC and NASSCOM FutureSkills certifications, supporting learners in building credibility and pursuing opportunities in the growing Artificial Intelligence industry.

With AI transforming industries and creating new career possibilities, gaining the right technical knowledge and hands-on experience has become increasingly important. DataMites Institute offers classroom-based Artificial Intelligence Courses in Ahmedabad, Hyderabad, Coimbatore, Bangalore, Chennai, Pune, Mumbai, and Delhi, enabling learners to build practical AI skills and prepare for emerging opportunities in the technology sector.