Machine Learning Engineer focused on building production-grade ML systems and scalable data platforms. Currently a Senior Software Engineer at NYU Urban Systems Lab, where I design scalable ML pipelines, data platforms, and cloud infrastructure for real-world climate and geospatial applications.

2024 — NowUrban Systems LabSenior Software Engineer

2024 — Now

New York, United States

Built scalable ML pipelines using TensorFlow and GCP Vertex AI for ConvLSTM/CNN models, reducing training latency by 35% across 10,000+ geospatial samples

Designed and automated ETL pipelines using Python, Terraform, and GCP Cloud Functions, reducing data processing time by 30%

Developed full-stack geospatial applications (Vue.js, MongoDB, APIs), improving system performance by 25%

Worked on production ML systems integrating data pipelines, model training, and deployment workflows

2024 — 2024Arizona State UniversitySoftware Engineer

2024 — 2024

Tempe, Arizona, United States

Engineered ARIMA and LSTM time-series forecasting models — reduced prediction errors 30% on power and water network data

Built TensorFlow deep learning model using PyPSA — 90% accuracy predicting cascading failures in power networks

Developed ETL pipelines in Python and Azure Data Factory processing real-time data from 50+ sensors; built 10+ Tableau dashboards integrated into ArcGIS for geographic visualization

Published: "Characterization of human extreme heat exposure" — Science of the Total Environment, Mar 2024 (ICB Biometeorology 2023, 30 countries)

2022 — 2023Arizona State UniversityGraduate Research Assistant

2022 — 2023

Tempe, Arizona, United States

Developed ETL pipelines in Python and Azure Data Factory to process real-time air quality data from 50+ sensors, while designing 10+ AWS-sourced Tableau dashboards, integrated into ArcGIS for geographic visualization.

2022 — 2022Arizona State UniversityResearch And Development Assistant

2022 — 2022

Tempe, Arizona, United States

Implemented ARIMA and LSTM time-series techniques, reducing forecasting errors by 30% and uncovering actionable insights, using Python libraries (OSMNx, Networkx, Pandas) to generate Water, Pump, and Power networks, enhancing efficiency by 20%.

Engineered TensorFlow deep learning model using PyPSA for 90% accurate prediction of cascading failures in power networks.

2020 — 2021Left Right MindJunior Software Engineer

2020 — 2021

Pune, Maharashtra, India

Analyzed business requirements for indirect direct tax automation, translating them into software features, and coordinated a 25-member team to build a highly scalable and reliable Azure Kubernetes Service(AKS) solution with 99% accuracy.

Achieved 40% retrieval time reduction through B-tree and bitmap indexing, refining response rates by 25% via query enhancement, and structured and maintained T-SQL databases with RESTful APIs using Flask, achieving a 3x increase in data retrieval rate.

Skilled GIT versioning, orchestrated agile teamwork with JIRA, and crafted PowerBI dashboards for 20% quicker insights.

Executed Angular 8 web development with Node.js backend, abbreviating deployment cycles 30% via Azure functions.

Education

2022 — 2023

Arizona State University

Master of Science - MS

2022 — 2023

2016 — 2020

L J Institute of Engineering and Technology (LJIET)

Bachelor's degree

2016 — 2020

Experience+

Education

Master of Science - MS

Bachelor's degree