About
The Story
I started out building enterprise backend systems — Spring Boot services and loan-origination workflows for a US credit union at Newgen Software — before a stint consulting for the World Wide Fund for Nature (WWF), where I helped run a sustainability education challenge that reached 54,000 students across 2,000 schools in India.
That mix of systems work and wanting to see the impact of what I build is what brought me to the University of Arizona for a Master's in Data Science. Since then I've gone deep on ML systems efficiency: researching federated learning at DASS Lab, writing CUDA kernels to squeeze a 156x speedup out of Mamba state space model inference, and shipping a production agentic LLM/RAG pipeline for McGraw Hill's Eller Immersion program.
When I'm not debugging convergence curves, you'll probably find me on a climbing wall.
Toolkit
Grouped by what it's for, not just what it is.
Languages
- Python
- C++/C
- Java
- R
- SQL
ML/DL Frameworks
- PyTorch
- TensorFlow
- CUDA
- scikit-learn
- Pandas
- NumPy
ML/AI Techniques
- Deep Learning
- Federated Learning
- NLP
- LLM
- RAG
- LoRA/PEFT
Backend & Infra
- SpringBoot
- Docker
- Kafka
- PySpark
- MongoDB
- Postgres
- pgvector
- HPC
- MLOps
- A/B Testing
Timeline
Research, coursework, and volunteer work — tagged by kind.
B.Tech in Electronics and Communications
Dr. Akhilesh Das Gupta Institute of Technology & Management, GGSIPU
Graduated with a GPA of 3.22, then moved into backend engineering.
Application Engineer
Newgen Software
Developed and integrated Spring Boot backend services and RESTful/SOAP APIs within Newgen's iBPS Retail Loan Origination System (RLOS) for a US credit-union client, enabling automated credential/KYC and credit-bureau checks. Built and managed the CFCU database in PostgreSQL and developed server-rendered JSP screens wired to Spring/Servlet controllers.
Consultant
World Wide Fund for Nature (WWF)
Executed the Wild Wisdom Global Challenge, engaging 54,000 students across 2,000 Indian schools. Ran strategic planning, workshops, and community-building activities that grew student engagement by 40% in 4 months.
MS in Data Science
University of Arizona
GPA 3.9. Coursework and research spanning ML systems efficiency, federated learning, and production LLM/RAG pipelines — from CUDA-level optimization to deploying agentic systems end to end.
Drive Chair, Basic Needs Centre (ASUA)
Built partnerships with 10+ campus organizations within 6 months, boosting food-donation community support by 200%.
Volunteer Graduate Researcher
DASS Lab, University of Arizona
Researching AI efficiency and federated learning (edge ML) under Prof. Jyotikrishna Dass. Replicated and stress-tested published federated fine-tuning setups (FFA-LoRA, FLoRA, FedIT) under homogeneous/heterogeneous settings in PyTorch on HPC clusters, and helped develop aggregation methods that improved performance by 30% and cut communication overhead by 70%.
What's Next
Wrapping up my MS in Data Science in December 2026 and continuing federated learning research at DASS Lab. Actively looking for full-time data science / ML engineering roles — especially ones touching production LLM systems, RAG, or ML infrastructure.