About Me
Applied Scientist
I specialize in architecting and deploying scalable machine learning solutions across Computer Vision, NLP, Deep Learning, and Generative AI. With over 7.5 years of experience and a Master's in Research from IIT Madras, I have a proven track record in technical leadership and translating cutting-edge research into production-ready solutions.
Currently at Amazon's Ads Trust team, I lead the development of multimodal content labeling systems, regulatory compliance pipelines, and billion-scale image retrieval platforms using VLMs, LLMs, and diffusion models.
I am also an avid photgrapher and highly interested in computational photography.
Work Experience
My professional journey in ML, AI, and software engineering.
Amazon
Applied Scientist - Ads Trust Team
- Led architecture for a multimodal content labeling system using VLMs achieving 90% coverage at 95% precision
- Architected regulatory compliance system for pharmaceutical ads
- Implemented real-time content moderation and billion-scale object-centric image retrieval platform
- Developed synthetic data generation pipelines and Generative Image Models
Verisk Analytics Inc.
Machine Learning Engineer - Global R&D Team
- Developed enterprise-scale document information extraction and synthetic document generation pipeline
- Architected pandemic modeling systems using agent-based network simulations
CodeNation LLC.
Software Development Engineer
- Deployed "ML Factory" - an AutoML platform for streamlined ML model training and deployment
- Built "AutoFix" - AI-based code quality improvement using code-specific knowledge graphs
- Developed time-series forecasting and anomaly detection models for multi-variate time-series
Find Me A Shoe
Intern
- Developed algorithm for foot measurement extraction from multi-view images using pose estimation techniques
Education
Academic background and research experience.
Indian Institute of Technology Madras
2014 – 2018
CGPA: 8.8/10M.S. in Computer Science & Engineering
Specialization in Computer Vision and Deep Learning. Thesis: "Data Augmentation using Part-based Deformations of Shapes." Also worked on Pedestrian Detection, Sketch-based Image Retrieval, Bi-modal Personality Analysis, and Image & Video Inpainting.
Institute of Technology, Nirma University
2010 – 2014
CGPA: 7.76/10B.Tech. in Information Technology
Strong foundation in algorithms, data structures, and software engineering fundamentals.
Projects & Publications
Research papers, competition wins, and featured work.
Data Augmentation using Part-based Deformations of Shapes
M.S. thesis research - using part analysis of shapes extracted from available images to augment labeled data, achieving improved CNN classifier performance for shape classification.
Personality Analysis from Interview Videos
Deep Learning solution for the "First Impressions" challenge by Chalearn Looking at People. Employed a novel training technique for videos - scored 2nd rank in the competition.
Technical Skills
Technologies, frameworks, and research domains.
Languages and Frameworks
Domains
MLOps / Cloud
Get in Touch
Have a question or want to collaborate? Let's talk!