Current Position and Field

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Q: Can you describe your current position and what field you work in?

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A: So actually, I completed my bachelor's in computer science. I graduated in 2017. I was in India at that time. Then I started my journey with IBM. I worked there for 1.5 years, and my domain was testing. So testing means just testing websites and giving the results and so on. I didn't like that work, but they gave me that job. So, I had to do it, right?

However, I wanted to move to a development role. I started giving more interviews for development roles, specifically for the Software Development Engineer (SDE) role. Eventually, I moved to another company, Amdocs in India, and worked there for around 1.5 years. After that, I moved to Adobe.

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So, I moved to Adobe because I wanted to work more on machine learning and data science, as you mentioned. This technology was something I used to work on personal projects as well. That's why Adobe hired me, even though I had some experience in machine learning but not that much. They wanted me to be part of an innovation team where I could use my development skills, machine learning, data analysis, visualization, and modeling altogether on their projects.

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At Adobe, I realized I should go for a master's in AI and do some research to gain more skills. So, after 1.2 years, I decided to pursue my master's and came to the United States. I started at Stevens in September 2022. After a month, I joined a lab focused on quantum materials, nano materials, and they wanted someone with AI knowledge to do research and experiments in the lab.

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While doing my master's and working in the lab, I started working on my own research and publishing papers. Side by side, I joined Handle Architect as a machine learning engineer. There, I got the opportunity to work on research plus machine learning, reading papers, and doing innovative work for them. I developed some tools based on generative AI, which are completely new for them, and I'm currently working on publishing those tools.

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So, this is all about my journey. I'm currently working in the lab, finishing my master's, and working at Handle Architect as an ML engineer. Next month, I will graduate and continue my job there.

 

Typical Day at Work

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Q: What does your normal day-to-day look like?

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A: It's like a hybrid schedule here. I go to the office from Monday to Wednesday, from 9 AM to 6 PM. On Thursday and Friday, I work from home, but I also work from the lab. I do experiments and office work from 9 AM to 6 PM, then do experiments until about 10 PM. On weekends, I finish assignments or projects and do writing for my papers. I also conduct experiments in the lab. So, my weekends are completely dedicated to the lab and assignments. This is my day-to-day life.

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Interest in Machine Learning

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Q: What got you interested in the work you're doing now, specifically machine learning?

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A: I'm more into the deep learning domain, specifically generative AI. I like using machine learning for real-life use cases. For example, when I joined the lab for quantum materials, I had zero knowledge about it. But I found that I could use deep learning to automate and support their research, making it useful for researchers. Similarly, in my architecture-based company, I'm using deep learning to help architects automate their work. I like using AI to make people's lives easier.

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Emerging Career Opportunities

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Q: In your current field, what do you think are the most interesting job opportunities emerging for people entering the job market in the next 5 to 10 years?

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A: Machine learning and AI are broad fields with many roles, like data analysis, data engineering, ML engineering, and research scientists. If you're a fresher and know some Python programming, databases like SQL, and maybe big data tools like Hadoop or Power BI, you can start as a data engineer or data analyst. If you enjoy programming and modeling, you should go for ML engineer roles. Research scientist roles are interesting but require research experience. For freshers, starting as a data engineer or analyst is a good idea if you have basic technical knowledge. For experienced individuals, showcasing your experience with real-life projects is crucial.

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Advice for Young Aspiring ML Engineers

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Q: What specific advice do you have for young people interested in your field of work, such as colleges to consider, companies to work for, or how to gain experience?

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A: If you're interested in machine learning or data science, you must know Python programming. You should also know at least one database, like SQL, and one visualization tool, like Matplotlib, Pandas, or Power BI. Additionally, understanding statistics and mathematics is crucial. As a fresher, focus on learning these skills to crack interviews and secure a job. If you're experienced, highlight your relevant experience and projects. For aspiring software developers, practice coding and problem-solving, while those interested in ML should strengthen their math and statistics knowledge.

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Importance of Calculus

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Q: How important is calculus in your field? Some machine learners say they use it daily, while others don't use it much at all. What do you think?

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A: It's not necessary to use calculus daily, but interviewers expect you to know basic concepts. You should understand probability, calculus, and statistics, including hypothesis testing. These are mandatory topics for machine learning roles. For data analysis or data engineering roles, being proficient in Excel, and handling large datasets is crucial. For coding, analysis, modeling, and prediction roles, you should have a solid grasp of mathematics and statistics.

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Career Reflections

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Q: Is there anything you would have done differently in your career journey so far?

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A: Apart from my main work, I enjoy blogging and vlogging. I create tutorial videos for learning purposes and have done some freelancing for startups. I've also created video courses for educational startups like Unacademy and Scalar Academy. These activities are something extra beyond my day-to-day job.

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Short-Term and Long-Term Plans

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Q: What are your short and long-term plans and goals?

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A: My long-term goal is to work in a research scientist role. I always wanted to work on research but never planned for a PhD. That's why I joined a lab and started publishing papers. Eventually, I want to work at Adobe again, focusing on research. For now, I'm focused on my current job, gaining experience, and publishing papers and innovative tools. My short-term goals include publishing papers and developing innovative products.

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Cutting-Edge Career

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Q: Why do you consider your career cutting-edge and what characteristics make it modern and up-and-coming?

A: AI is often seen as cutting-edge technology, but it's about using it to make life easier. AI can be challenging, and it's not a complete solution, but it can help in many ways. For example, I'm using AI in architecture and materials research to make researchers' and architects' lives easier. AI can help people by automating tasks and providing innovative solutions, but it has its limitations.

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Most Useful Programming Language

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Q: As a student currently taking a programming class in Java, what programming language do you think is the most useful to learn, and how is it applicable to your work?

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A: I started my career with Java and later learned Python. Both have their advantages. Java is good for building large, modular, and scalable applications. Python is better for machine learning due to its extensive libraries. Java is useful for backend applications, while Python is ideal for machine learning, image processing, and dealing with diverse data types. Both languages are useful depending on the context.

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Machine Learning in Quantum Mechanics

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Q: How did you use machine learning in quantum mechanics and materials research?

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A: I worked with 2D quantum materials, specifically transition metal dichalcogenides (TMDs). To analyze the properties of these nano-sized materials, we used special microscopes and Raman spectroscopy. I used deep learning to automate the analysis of these properties from microscopy images, making the process faster and more efficient.

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Machine Learning in Architecture

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Q: How do you use machine learning for architecture, like designing floor plans?

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A: I use generative AI to design floor plans and interiors. You provide the boundary of the floor plan, and the AI generates the interior layout. I'm also working on tools to find similar plans from a database and generate new ones using graph theory. These tools are aimed at automating and improving the design process in architecture.

 

Differences Between ML, AI Engineering, and Data Science Roles

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Q: What are the differences between machine learning, AI engineering, and data science roles?

 

A: AI is the overarching field, with machine learning and deep learning as subfields. Deep learning is good for handling image, text, and audio data. Machine learning focuses on tasks like regression and classification using numerical data. AI engineering roles involve developing and deploying AI systems. Data science roles often focus on data analysis, visualization, and extracting insights from data. Each role has its own focus and skill set requirements.