Pradeep K. Pant
Based in Bengaluru, India · Originally from the lower Himalayas (हिमालय)
About Me
I am a Software Architect and AI/ML practitioner with a deep interest in building systems that solve real-world problems. I enjoy working at the intersection of software engineering, machine learning, and research, and I believe programming is a powerful medium for turning ideas into reality.
I began this blog in the autumn of 2007 to chronicle my journey and share thoughts on the ever-evolving world of technology. Over the years, it has grown into a space where I explore ideas, document experiences, and share learnings from both professional and personal pursuits.
Alongside my professional interests, I am deeply passionate about trail running and endurance sports. When I'm not working with code or research, you'll usually find me running in the hills, exploring nature, or writing about my experiences here.
Education
Professional Journey
My professional journey spans over two decades and reflects a gradual evolution from systems development and academia to enterprise software and AI/ML.
From 1997 to 2005, I was actively involved in teaching and academic roles at institutions such as Birla Institute of Technology, Mesra, Birla Institute of Applied Sciences, and BITS Pilani — teaching programming, electronics, digital design, microprocessors, and computer systems. Alongside teaching, I was also involved in developing prototypes and providing technical support for small-scale industries, working on digital systems, microprocessor interfacing, and software-based solutions.
In 2005, I transitioned fully into the IT industry, working across enterprise software and product engineering roles at organisations such as Xerox Inc, Wokana Technologies, and Ockham BV.
I currently work as an AI/ML & Generative AI Architect at LTIMindtree, focusing on building scalable, production-grade AI systems for enterprise environments.
How I Can Help
Helping teams move from experimentation to robust, production-grade AI systems with emphasis on reliability and long-term maintainability.
Building and improving pipelines for time-series forecasting, monitoring, and model lifecycle management.
Guiding the practical use of ML, Generative AI, and LLM-based systems in enterprise environments focusing on value creation over hype.
Bridging academic research and industry practice in process mining, concept drift, and explainability.
Supporting team building, mentoring engineers and data scientists, and strengthening technical culture.