I'm a data engineer who got restless just building pipelines. Over time, I found myself pulling together data infrastructure, AI tooling, and automation into one skillset because honestly, that's where the most interesting problems live.
At my core, I work with production data systems. I own 50+ Airflow DAGs, move data between MongoDB and PostgreSQL daily, and I've spent real hours deep inside databases with 125M+ rows figuring out why things are slow and making them fast. Partitioning, index cleanup, query tuning, all of it. Not textbook stuff, but learned-the-hard-way, production-tested stuff.
What makes me a little different is that I didn't stop at infrastructure. I started building with LLMs and AI APIs, setting up MCP servers, and creating tools where business teams can just ask questions and get answers without filing a ticket. I write coding challenges that push AI agents to their limits, so I understand both sides of the equation: what these models can actually do, and where they fall apart.
On the automation side, I've replaced manual reporting workflows, built settlement calculation engines, and wired up dashboards that refresh themselves. The goal is always the same: take something that eats up someone's time every week and make it disappear.
I also build on the web when the project calls for it. Flask APIs, React interfaces, data crawlers, internal tools. Whatever gets the job done.
Here's the short version of why I can deliver on all four of these services:
I've been doing this work in production, not in tutorials. I manage real pipelines, optimize real databases, build real AI integrations, and ship real automation. I think in systems, I care about reliability, and I don't overcomplicate things.
If you need someone who can connect the dots between your data, your AI ambitions, and the actual infrastructure that holds it all together, that's exactly where I work best.