Job Description and Industry Focus

​​

Q: Could you tell us a little about your current position and the industry that you work in?

A: Currently, I work as a machine learning engineer at a startup, where our primary focus revolves around predicting power outages for major telecommunications companies worldwide. It's a fascinating field that merges mathematics, calculus, statistics, data analysis, and computer science into the realm of machine learning. Essentially, my role involves developing algorithms that can detect and forecast power outages before they occur, which is crucial for ensuring uninterrupted services for these telecom giants.

 

Typical Day at Work
 

Q: What does a typical day at work look like for you?

 

A: A typical day for me usually kicks off with several meetings, including sessions with my manager and clients. These meetings are pivotal as they help us align on the day's tasks, discuss ongoing projects, and plan for the upcoming week in sprints. We dive deep into data analysis, algorithm development, engage in brainstorming sessions with fellow ML engineers, delve into research papers, write and validate code, and ensure that our algorithms are robust and effective.

 

Career Journey Breakdown

 

Q: Could you give us a breakdown of your career journey

A: My journey in this field began with my undergraduate studies in India, followed by pursuing a master's degree at New York University's Kurant Institute for Mathematical Science. During my academic journey, I explored various internship opportunities across software development, cybersecurity consultancy, machine learning engineering, and data engineering. This phase of exploration was crucial in helping me discover my passion for machine learning and entrepreneurship. It paved the way for me to specialize in computing entrepreneurship and innovation during my master's program, where I also contributed to open-source AI models and published research papers, which significantly contributed to my career growth as a machine learning engineer.

 

Future of Machine Learning

​

Q: What do you believe the future of Machine Learning is?

​

A: Looking ahead, the next 5 to 10 years hold immense potential for the field of machine learning. We are witnessing the emergence of Gen. AI, which marks a significant leap from the research phase to practical implementation. Companies, including ours, are leveraging generative AI for various applications. I have had the opportunity to work with startups and consult with companies, contributing to the advancement of generative AI technologies. In the coming years, we can expect Gen. AI to expand its capabilities beyond text, audio, and video applications, potentially integrating robotics and leveraging quantum computing advancements. The convergence of AI and quantum computing is a fascinating prospect that promises groundbreaking developments in the field.

 

Advice for ML and AI Enthusiasts
 

Q: Do you have any advice for young ML and AI Enthusiasts? 

 

A: For those interested in machine learning and AI, my advice would be to focus on building a strong foundation in mathematics, particularly calculus, linear algebra, and data structures. These concepts form the backbone of AI algorithms and are indispensable in developing robust models. Platforms like LeetCode provide excellent opportunities to practice coding and enhance logic-building skills. Additionally, staying curious, exploring new technologies, and continuously learning are key aspects of thriving in this dynamic field.

 

Linking Calculus with Machine Learning
 

Q: In what ways does calculus link to Machine Learning?

 

A: Calculus plays a crucial role in machine learning, especially in optimization techniques such as gradient descent and minimizing loss functions. Understanding calculus helps in fine-tuning ML models, optimizing performance, and achieving more accurate predictions. It's like solving puzzles where calculus provides the tools to find the best solutions and improve algorithm efficiency.

 

Career Reflections and Goals
 

Q: If you could change one thing about your career journey what would it be?

 

A: Reflecting on my career journey, if I could change one thing, I would have delved deeper into data structures and algorithms earlier in my education. Strengthening these fundamentals from high school onwards would have provided a more solid groundwork for my journey in machine learning. Currently, my short-term goal is to contribute to the success of my startup venture, while in the long term, I aim to establish multiple sources of income through creating and scaling startups both in India and the US.

 

ML vs. Data Science
 

Q: What is the difference between Machine Learning and Data Science?

 

A: Differentiating between machine learning and data science, machine learning engineering focuses more on developing algorithms and optimizing models, while data science involves analyzing and interpreting data to derive insights and make informed decisions. Both fields complement each other, and individuals can choose based on their interests and career objectives.

 

Final Thoughts on the AI Journey
 

Q: Lastly, can you talk about your overall experience working with ML and AI?

​

A: The journey in the field of AI and machine learning has been incredibly rewarding. It's a realm filled with continuous learning, innovation, and the excitement of shaping the future of technology. As technology evolves, embracing new challenges and opportunities fuels my passion for driving innovation and making meaningful contributions to the AI landscape.