I build and ship machine learning systems that live in real products, not just notebooks. My work spans generative models, computer vision and data platforms, from early prototypes through to production rollouts. I care about reliability, latency and visibility as much as raw model quality, and I like working close to the people who use the system. Most days I am turning vague ideas into concrete experiments, milestones and deployments that move a product forward.
Experience
Chief AI Engineer, Plexor
Oct 2025 to Present
Lead development of advanced object detection and vision-language models for real-time retail surveillance.
Architect scalable AI pipelines for seamless edge deployment in retail environments.
Enhance detection precision using innovative vision-based techniques.
Drive SOTA object detection and VLMs for top tier surveillance.
Junior Specialist, University of California, Riverside
Jul 2024 to May 2025
Engineered deep learning models for mosquito orientation classification, achieving 96% accuracy across four orientations.
Utilized advanced image processing techniques including thresholding, edge detection and morphological transformations for robust biological feature extraction.
Integrated model deployment pipelines with interdisciplinary teams, optimizing end-to-end system performance and scalability.
Enhanced scalability of feature extraction pipelines, ensuring adaptability for future biological studies.
Documented research findings and contributed to publications, sharing insights with the scientific community.
Research Engineer, University of Guelph
Jan 2023 to Jun 2023
Utilized image segmentation with LLM-based text analysis to assess soil properties, improving accuracy by 18%.
Designed an automated application, reducing data processing time by 40% and enhancing indicator detection accuracy by 25%.
Collaborated with soil scientists to refine model parameters for precise results.
Integrated feedback from cross-disciplinary teams to ensure model usability for practical applications.
Co-authored a paper on data-driven soil characterization, contributing to a 15% performance boost.
Computer Vision Researcher, Avawatz, UT Dallas, IIT Bombay
Mar 2022 to Jul 2022
Created deep learning algorithms for autonomous vehicles, boosting object detection accuracy by 15% using Faster R-CNN and YOLO.
Enhanced segmentation accuracy by 20% with 3D simulations, improving off-road vehicle performance.
Developed a preprocessing pipeline, reducing training time by 25% for large datasets.
Collaborated with a multidisciplinary team to refine detection algorithms for varied terrain types.
Integrated machine learning models into real-time applications, optimizing system responsiveness.
Machine Learning Engineer, Lokavidya, IIT Bombay
Jan 2021 to Sep 2022
Built knowledge curation engines for large datasets by applying ML/NLP techniques, optimizing pipelines to improve search relevancy and information retrieval.
Analyzed unstructured data to develop classification models, leveraging feature engineering and hyperparameter tuning to enhance model accuracy.
Designed clustering algorithms for dataset categorization, improving downstream task efficiency and reducing data redundancy.
Automated anomaly detection in large-scale datasets, achieving a 20% reduction in manual analysis overhead and streamlining data verification processes.
Optimized indexing strategies in data pipelines, reducing query response times by 35% and improving system performance for real-time queries.
Skills
Technical Skills
Deep Learning
NLP
LLMs
Computer Vision
Generative Modeling
Diffusion Models
Machine Learning
Reinforcement Learning
Data Analysis
Data Visualization
Statistics
Graph Neural Networks
Platforms & Tools
Python
C++
C
Keras
TensorFlow
PyTorch
PyTorch Lightning
FastAI
OpenCV
Numpy
Matplotlib
Pandas
Hugging Face
SQL
Flask
BeautifulSoup
XGBoost
Tableau
MLOps
GCP
AWS
Kubernetes
Docker
Git CLI
Jupyter Notebooks
Anaconda
Linux Terminal
ROS
CUDA
MPS (Apple)
Apache Spark
Tensorboard
Firebase
MS Azure
MATLAB
Projects & Papers
ApollodB: Psychoacoustic EQ Generation System
SOTA Project • 2024 to Present
Built an open-source TensorFlow-based system on Google Cloud for psychoacoustic EQ generation, achieving state-of-the-art results on the DEAM dataset.
Developed advanced neural networks to generate personalized audio equalization profiles based on psychoacoustic principles.
Implemented cloud-based deployment using Google Cloud Platform for scalable real-time audio processing.
Created a comprehensive demo interface for interactive testing and validation of EQ generation capabilities.
Open-sourced the project to contribute to the audio engineering and machine learning communities.