I'mSehastrajit
Building scalable ML systems at the intersection of LLM inference, computer vision, and cloud-native pipelines. M.S. CS @ ASU · 4 published papers · shipped at scale.
01 / About Me
The Human Behind the Models
AI/ML Engineer pursuing a Master's in Computer Science at Arizona State University. My work spans LLM systems, large-scale computer vision, and cloud-native data pipelines — built to scale, built to ship.
I design end-to-end ML workflows: from data ingestion and feature engineering through model training, optimization, and production deployment. I care about measurable impact — not just accuracy metrics, but latency, reliability, and real-world adoption.
Formerly a Machine Learning Engineer at Omdena (where I pushed model accuracy from 72% → 89%), a Research Data Scientist at Auburn University, and a Research Aide at ASU's School of Sustainability building geospatial CV pipelines for solar infrastructure analysis.
Education
02 / Experience
Where I've
Built Things
Research Aide
ASU School of Sustainability
- Built computer vision and geospatial pipelines for temporal photovoltaic (PV) infrastructure analysis across multi-year NAIP imagery
- Automated preprocessing, alignment, and cross-year comparison of multi-resolution satellite imagery (0.3m–0.6m) for longitudinal infrastructure monitoring
- Quantified image fidelity and spatial consistency using PSNR, SSIM, RMSE, and MAE to support large-scale sustainability research
03 / Projects
Things I've Shipped
End-to-end ML systems — from architecture design to production deployment and benchmarking.
TITANS: Memory-Augmented LLM
Designed and deployed an optimized memory-augmented LLM inference stack with measurable retention and latency gains, inspired by the TITANS architecture paper.
- Custom memory-augmented transformer achieving 35% improvement in long-context retention on BoolQ and GSM8K
- Deployed optimized LLM inference stack using Triton, vLLM, and FastAPI for low-latency serving
- Reduced inference latency by 30%+ through quantization, batching optimization, and benchmarking
Cooling Tower Detection from NAIP Imagery
Computer vision pipeline for detecting cooling tower locations and estimating physical dimensions from aerial NAIP imagery to support sustainability research.
- Geospatial image processing pipeline for pixel-to-real-world spatial measurement conversion
- Large-scale aerial imagery processing with automated preprocessing for infrastructure analysis
- Integrated with ASU School of Sustainability's longitudinal monitoring research
Blue Zones Longevity ML Platform
Scalable ML analytics platform for analyzing longevity and lifestyle factors using vLLM, inspired by Netflix's 'Live to 100'. Led a team of 15 at Omdena.
- 72% → 89% predictive accuracy improvement through feature engineering and pipeline optimization
- 40% preprocessing latency reduction with structured logging over 1M+ records
- Actionable public health insights for policy recommendations on longevity factors
04 / Skills
The Full Stack
From raw data to production-grade ML systems — across languages, frameworks, and cloud platforms.
Languages
ML & AI
LLM & MLOps
Cloud & Systems
07 / AI Experience
Meet Luna
Not a generic chatbot. Luna is purpose-built for this portfolio — she generates pitches, matches job descriptions, writes cover letters, and answers anything about Sehastrajit. Powered by Groq's fastest inference.
05 / Publications
Peer-Reviewed Research
4 published papers across IEEE, international journals in AI, medical imaging, and bioinformatics.
Pipelined Structure in the Classification of Skin Lesions Based on AlexNet CNN and SVM Model With Bi-Sectional Texture Features
IEEE Access
Comparative Performance of Deep Learning Architectures in Classification of Diabetic Retinopathy
International Journal of Ad Hoc and Ubiquitous Computing
An Insight on Recent Advancements and Future Perspectives in Detection Techniques of Parkinson's Disease
Evolutionary Intelligence
The Estimation of Statistical Features from VMD Levels for Automated Sleep Apnoea Classification
International Journal of Bioinformatics Research and Applications
06 / Contact
Let's Build Something
Open to ML engineering roles, research collaborations, and interesting problems at the intersection of LLM systems, computer vision, and production AI.
sehastrajit@gmail.com · Greater Phoenix Area