Curiosity, open source, and engineering for the real world.
I'm Chinmaya — an engineer whose work has moved from application development into cloud, platforms, and applied AI. This is the story behind that path, the work I find interesting, and the principles I bring to it.
01Where it started
Curiosity came first. Open source gave it direction.
I was fascinated by computers as a child. My experience spans from the days of Windows 95 to today, across Windows, GNU/Linux, macOS, and more. I learned and worked on computers every chance I got, following that curiosity long before I knew where it might lead.
During my undergraduate years, I found the GNU/Linux Users Group community. It brought me closer to open source — not only the software, but the habit of learning in public, sharing what you know, and building alongside other people.
Open source roots
- —Chaired the VNR GNU/Linux Users Group as an undergraduate.
- —Helped organize FOSS events across the region with the Swecha community.
- —Co-organized the 2010 National FOSS Convention, inaugurated by Dr. APJ Abdul Kalam, the beloved former President of India.
02The path here
From application code to the systems that keep it running.
My career began in application development and testing, then widened through systems administration, cloud engineering, and platform work. That path taught me to see software as more than the code that implements it: it is also deployment, identity, networking, data, observability, security, and the people responsible for it in production.
Today I work across that whole surface. I build services in Python and Go, automate infrastructure, operate Kubernetes platforms, design delivery pipelines, and connect systems that were not originally built to understand one another.
03The work
The interesting problems live where software meets operations.
Much of my work has been in systems that need to remain understandable and dependable as they grow: cloud-native applications, Kubernetes platforms, enterprise integrations, identity services, event-driven pipelines, and the tooling that lets other engineers ship safely.
I have worked across AWS, Azure, and Google Cloud, and in environments where reliability, security, and careful handling of sensitive data are part of the design rather than an afterthought. I enjoy the space between application and infrastructure engineering because decisions there shape how a system behaves for everyone who builds, operates, and relies on it.
A few threads through the work
- —Migrating enterprise integrations to Kubernetes and building the automation around them.
- —Operating federated identity and single sign-on services for a large university community.
- —Designing infrastructure, GitOps workflows, observability, and delivery systems across multiple clouds.
- —Building production services and APIs from backend implementation through cloud deployment.
04Now
Learning what AI can do — and where it still needs engineering.
These days I am learning all things AI. The field moves quickly enough that keeping up can feel like a project of its own, but that pace is also what makes it interesting.
My focus is practical: retrieval-augmented generation (RAG), agentic systems, Model Context Protocol (MCP), multimodal inputs, and the work required to move an experiment into a useful production system. I am also building personal projects in this space. As they take shape, I will write about what worked, what did not, and what I learned here on the blog.
Selected milestones
- —Published research on dynamic hand-gesture recognition using LSTM and CNN architectures at IEEE ICMLA 2017.
- —Presented Ohio University's Kubernetes platform work at OHECC 2022.
- —Certified Kubernetes Administrator, with a background spanning software, cloud, platform, and applied machine learning.
05Working principles
Build for the day when things go wrong.
- —Logs are like insurance. Nobody wants to pay for them when everything is going well; when something goes wrong, everyone wants the best coverage. Prepare before the moment of need.
- —Ownership does not end when a service is deployed. It includes how the service is operated, observed, supported, and improved.
- —Observability is paramount. A production system should make its state and its failure modes understandable.
- —Take time to architect the system before writing the first line of code. Good boundaries and deliberate tradeoffs pay for themselves over the life of the system.