Parth Agarwal

Led the development and deployment of multiple Computer Vision products and MLOps initiatives, driving innovation and efficiency in AI/ML operations.

• Initiated and delivered a Liveness Detection product from concept to execution, distinguishing live persons from presentation attacks and handling ~3M API calls/month, preventing fraud.
• Achieved performance surpassing competitors' existing products within 1 year by improving model accuracy by ~12% and reducing False Rejection Rate by >40% through advanced data generation and model optimization.
• Headed the MLOps initiative, deploying MLflow for experiment tracking and data annotation, streamlining workflows for 20+ Data Scientists, 6 Data Annotators, and Product Managers.
• Authored 2 packages to standardize MLflow usage and streamline regression testing for Data Science product APIs, enhancing codebase efficiency.
• Engineered a Video Liveness product from scratch, incorporating 25+ API calls and creating scoring mechanisms, reducing dependency on vendor products and cutting costs by ~45%.
• Developed a generic LLM platform that reduced Turnaround Time (TAT) for new product requests from ~2 weeks to 1 day and cut costs by ~90%.
• Led a 6-person team, establishing the Data Science team and overseeing multiple projects including Face Verification, Face Detection, and Image Quality Assessment.

Developed and implemented automated ETL pipelines for diverse raw data sources and OLAP design, ensuring efficient data migration and processing.

• Designed and created automated ETL pipelines, integrating a variety of raw data sources and optimizing for OLAP design requirements.
• Successfully implemented a robust ETL pipeline to migrate critical client data from existing RDBMS to AWS Redshift Cluster, facilitating OLAP creation.
• Leveraged a suite of AWS services including S3, Lambda, and Redshift, alongside Apache NiFi, to ensure seamless and efficient data migration processes.
• Completed intensive 6-month training in Software Development Engineering (SDE) and advanced Big Data concepts, enhancing foundational technical expertise.

Recognized as a top performer for Q2 2022 at Karza Technologies for exceptional contributions.

Achieved finalist status in the prestigious OpenCV AI Competition in 2021, showcasing advanced AI/ML capabilities.

Recognized as Employee of the Month during tenure at ToTheNew in 2019 for outstanding performance.

Consistently achieved top rankings in competitive coding, hackathons, and robotics events from 2016 to 2018, demonstrating strong technical prowess.

Authored deep learning-based research utilizing CNNs to map poverty in India, fine-tuning an Inceptionv3 model on aerial and nightlight images as a proxy for economic status, published in IJRTE, vol. 8, no. 3.