Work Experience
My professional journey in machine learning, software engineering, and full-stack development. Each role has contributed to my expertise in AI, computer vision, and scalable system design.
Software Engineer Intern
Filmic Technologies
Current PositionLeading full-stack development of digital media platform including blockchain payments connecting philanthropists and archivists.
Key Achievements:
- Architected and deployed full-stack MVP from scratch using React, Next.js, and Nest.js on AWS EC2 and AWS S3 with CloudFront CDN, delivering a complete digital media platform
- Engineered comprehensive platform features including multimedia uploads, real-time chat functionality (web-socket), admin panel, and role-based access control for three distinct user types in 3 months
- Led intern development team by defining important features, deciding project priorities and timelines, delegating technical tasks, and mentoring fellow interns on implementation strategies
Technologies Used:
Machine Learning Engineer
Learno.AI
Spearheaded AI/ML development for education technology platform, enabling Y Combinator presentation.
Key Achievements:
- Spearheaded the MVP development from technical roadmap to execution within a 2-month timeframe using Streamlit, FAISS, and llama-index which enabled the co-founders to present the MVP to Y combinator
- Engineered a PDF parsing solution for visually complex documents by merging vision grid transformers along with traditional PDF parsers leading to a mAP of 0.82 @ IOU [0.50:0.95] on the Doclaynet dataset
- Orchestrated the entire lifecycle of the Python backend for an education chatbot, encompassing coding agents, setting up vector databases (OpenSearch), and establishing CI/CD pipelines for automated deployment
Technologies Used:
Machine Learning Engineer
Signimus
Specialized in computer vision and image processing with focus on advanced ML model optimization.
Key Achievements:
- Fine-tuned MODNet, a state-of-the-art image matting model by training it on a private dataset, leading to an 8.27% improvement in segmentation accuracy on images of people
- Developed a robust AWS pipeline for a memory and storage intensive application by setting up EC2, S3, CUDA drivers, and necessary monitoring alerts, resulting in responsive and scalable application
- Implemented dozens of visual-rich image filters by leveraging techniques like median and gaussian filtering, along with optimized open source GANs like StyleGAN, Pix2Pix, and more
Technologies Used:
Machine Learning Engineer
Expertrons
Developed recommendation systems and optimized database performance for large-scale user platform.
Key Achievements:
- Coordinated the development of an entire recommendation system for mentor matching, using a combination of content-based and collaborative filtering, offering personalized recommendations for 250K active users
- Optimized API processing time for short videos (reels) by 90% by indexing the MongoDB collection, improving overall feature efficiency, and subsequent user interaction
- Collaborated with the Tech Lead and Product Manager to develop multiple deep-learning models for enhancing user videos and pictures using image segmentation reducing the QC team's daily manual labor hours by 80%
Technologies Used:
Professional Skills
Skills developed and refined through professional experience
Machine Learning
Deep expertise in ML model development, computer vision, NLP, and AI system architecture
Full-Stack Development
End-to-end application development with modern frameworks and cloud infrastructure
Team Leadership
Technical leadership, mentoring, and cross-functional collaboration experience