Recently, I had the unique opportunity to be interviewed on a podcast hosted by my friend and former colleague. As I stepped into this new role as the interviewee, I was excited yet anxious about sharing my journey into machine learning (ML) and my experiences with job interviews, particularly at Intel. Little did I know that this would turn into a delightful mix of insights and humorous blunders.

🎥 The Setup: Lights, Camera, Action!

As the podcast began, I felt a rush of adrenaline. I was ready to share my story—how I transitioned into data science and the skills necessary for ML roles. However, as soon as the camera rolled, I found myself fumbling over non-technical questions. It’s one thing to discuss algorithms and data sets, but when asked about my journey! It was like being asked to recite Shakespeare while juggling flaming torches—definitely not my forte.

🤦‍♂️ The Hiccups Were Real

Throughout the interview, there were plenty of hiccups. From awkward pauses to mispronouncing terms, I found myself in a comedy of errors. At one point, I accidentally mixed up technical jargon with everyday phrases, leaving both me and my friend chuckling. It was a reminder that even in professional settings, it’s okay to laugh at oneself. After all, who doesn’t love a good blooper reel?

📚 Sharing My Journey

Despite the stumbles, I managed to share key insights from my journey:

• Educational Influence: I discussed how my academic background laid the foundation for my interest in machine learning. Exploring diverse subjects early on helped me discover my passion for data science.

• Real-World Applications: My fascination with real-world applications—especially in computer vision—was a major driving force in my career. This curiosity led me to seek hands-on projects that bridged theory with practice.

• Essential Skills: I emphasized that mastering mathematics and programming is critical for anyone looking to excel in ML roles. Whether you’re coding in Python or diving into C/C++, having a solid grasp of these skills is non-negotiable.

• Interview Preparation: Sharing tips on preparing for job interviews was particularly important. Understanding the job requirements and practicing both technical and behavioral questions can make all the difference—trust me; I learned this the hard way!

• Emergence of ML Compilers: We also touched on the rise of ML compilers and their significance in optimizing model efficiency. This niche area is becoming increasingly relevant as companies look to enhance their AI applications.

🌐 The Future Looks Bright

As we wrapped up our conversation, I highlighted the exciting future of AI and generative intelligence. These trends emphasize adaptability in the workforce—a lesson that resonated with me throughout my career journey. The ability to collaborate and continuously learn will be vital as technology evolves.

🤝 A Call to Action

So there you have it! My first podcast experience was filled with laughter, learning moments, and a generous dose of self-mockery. If you’re considering diving into podcasting or just want to hear about job interview experiences at Intel, I invite you to check out the full video. You’ll definitely gain valuable insights that could help you navigate your own career path.

Hello! I’m Himanshu Upreti, a Machine Learning Engineer at Google based in Bangalore, India. Previous to that I have experience of AI Engineer in Intel and Qualcomm. I was born in the beautiful state of Uttarakhand, India.

About Me

I hold a Master’s degree in Computer Science and Engineering from the prestigious Indian Institute of Technology Bombay (IIT Bombay). With a strong passion for technology and innovation, I’ve been actively contributing to various projects and research in the field of computer science.

My day-to-day work involves tackling exciting challenges in Natural Language Processing (NLP), Computer Vision (CV), and Machine Learning (ML). I am deeply interested in optimizing and deploying models using ONNX, leveraging the power of Git for version control, and exploring the capabilities of PyTorch for advanced research. I’m also proficient in C++, Python, and other technologies that help me create efficient and scalable solutions.

Work Experience

During my time at Intel Bengaluru, I have been involved in development of pytorch-distributed modules for Intel’ Gaudi accelerators.

During my time at Qualcomm CR&D, I’ve been involved in cutting-edge projects of AI100 that push the boundaries of cloud technology. My expertise includes:

• Developing and optimizing advanced algorithms in C++ to optimize neural networks.
• Optimizing source code of SOTA neural network for optimized peformance on AI100
• Designing and developing Graph Neural Networks projects
• Leading cross-functional teams to deliver successful projects.
• Designing optimized solutions to showcase Qualcomm’s performance in MLPerf.
• Collaborating with researchers and engineers to drive innovation and improvements in Qualcomm’s AI100 SDK.

Education

• Master of Technology (M.Tech) in Computer Science and Engineering

  • Indian Institute of Technology Bombay (IIT Bombay)

• Bachelor of Technology (B.Tech) in Computer Engineering

  • GB Pant University of Agriculture and Technology (GBPUAT)

Location

I am currently based in Bangalore, India, where I enjoy being part of a vibrant tech community and contributing to the city’s technological advancements.

Hobbies and Interests

Outside of my professional life, I have a passion for photography. Capturing moments and telling stories through images has always fascinated me. You can find some of my photography blogs.

I also believe in the importance of staying fit and active. In my free time, you’ll often find me at the gym, where I engage in various physical activities to maintain a healthy lifestyle.

Thank you for visiting my portfolio page. If you have any inquiries or would like to connect, feel free to reach out!

Follow me on LinkedIn for updates and insights into my work. You can book a 1:1 session with me on Topmate