ML & systems developer.
Passionate about good architecture.
building production ml systems, real-time backends, and infrastructure that scales. interests span machine learning, algorithmic trading, blockchain, and technologies that push systems to their limits.
quantitative developer — ml, trading systems, and backend infrastructure.
cs engineer with treasury and financial systems expertise. currently developing quantitative algorithms and backend infrastructure for trading systems. hands-on experience with SAP BTP, AI-driven treasury forecasting, cashflow prediction, and enterprise platform architecture. python, tensorflow, rust, c++—focused on building scalable financial systems without overengineering. bengaluru-based.
experience & craft.
treasury and financial systems expertise. quantitative development, ml engineering, and enterprise platform architecture on SAP BTP. focused on treasury forecasting, cashflow optimization, low-latency execution, and scalable financial infrastructure.
experiments.
interactive demos and research tools built alongside my main work.
Backtesting Engine
Run quantitative strategies entirely in-browser. Multi-exchange support, walk-forward optimization, Pyodide runtime.
more experiments in progress — join the waitlist
building systems that scale — ml, trading, backends.
common questions.
ML model development, inference pipeline engineering, algorithmic strategy development, backtesting infrastructure, and real-time data pipelines for equities, cryptocurrencies, forex, and commodities. I work best on projects where rigour matters — where model accuracy and a 10bps edge are both worth engineering properly.
I handle Indian and international equities, including NSE/BSE, NYSE, SSE, and LSE, along with cryptocurrencies, forex, and commodity futures such as gold and oil. My work covers tick-data ingestion, OHLCV time-series storage, and strategy simulation across markets, depending on data/API access.
I combine deep learning, machine learning engineering, quantitative mathematics, and fast news analysis to build and explain market reaction models. The thesis is that everything is linked: rates, indices, equities, crypto, forex, commodities, and news all feed into each other, so I train models to capture dependencies rather than isolated signals.
Yes — I have experience taking work through to publication (VeriGuard was accepted on first submission). If you have a dataset and a research question at the intersection of ML and finance, I am interested.
Let's talk.
open for ml engineering roles, quant research positions, and research collaborations — model development, trading systems, and inference infrastructure, especially where deep learning and financial markets intersect.
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