# Rajan Jain

> Senior Software Engineer @ NYU | Machine Learning Engineer | ML Systems, Applied AI, Data Platforms | TensorFlow, Python, GCP Vertex AI

Location: New York City Metropolitan Area, United States
Profile: https://flows.cv/rajanjain

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.

My work includes building TensorFlow-based ConvLSTM systems on GCP Vertex AI, processing 10,000+ samples, and reducing training latency by 35% through optimized data pipelines and distributed training.

I specialize in:

ML Systems & Applied AI
Data Engineering & ETL Pipelines
LLMs & Agent-based architectures (actively building)
Cloud-native ML (GCP, AWS, Azure)

I’ve worked across the full stack — from data ingestion and feature engineering to model training, deployment, and frontend systems.

I’m most interested in roles where machine learning meets real-world systems — AI platforms, ML infrastructure, and applied AI products.

Portfolio: https://rajanjain.vercel.app/

GitHub: https://github.com/Rajanjain98

## Work Experience
### Senior Software Engineer @ Urban Systems Lab
Jan 2024 – Present | 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

### Software Engineer @ Arizona State University
Jan 2024 – Jan 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)

### Graduate Research Assistant @ Arizona State University
Jan 2022 – Jan 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.

### Research And Development Assistant @ Arizona State University
Jan 2022 – Jan 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.

### Junior Software Engineer @ Left Right Mind
Jan 2020 – Jan 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.

### Software Engineer Intern @ Ordex Technology Solution Inc
Jan 2020 – Jan 2020 | Ahmedabad, Gujarat, India
- Worked on Angular 8 and Typescript language to design more than 30 responsive frontend screens for a website.
- Interpreted and debugged the code to solve the issue raised in the UAT environment.
- Contributed to the mobile team to redesign the angular code in Ionic language to handle cross-platform mobile applications.


## Education
### Master of Science - MS in Information Technology
Arizona State University
Jan 2022 – Jan 2023

### Bachelor's degree in Information and Communication Technology
L J Institute of Engineering and Technology (LJIET)
Jan 2016 – Jan 2020


## Contact & Social
- LinkedIn: https://linkedin.com/in/rajanjain76

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Source: https://flows.cv/rajanjain
JSON Resume: https://flows.cv/rajanjain/resume.json
Last updated: 2026-04-01