Pavitra B - Software Engineer | Data Analyst | Applied AI Engineer - WebCardio (Gadgeon Medical Systems)

Summary

Results-driven Software Engineer with 3+ years in cloud-native data processing, scalable ML infrastructure, and analytics platforms on AWS. Optimized distributed pipelines to significantly reduce processing latency and enable comprehensive analysis of extensive production data. Leverages expertise in backend engineering, data analytics, and applied AI to create high-performance systems that support strategic decision-making.

Overview

4

years of professional experience

Work History

Software Engineer | Applied AI & Data Platforms

WebCardio (Gadgeon Medical Systems)

09.2022 - Current

Architected and scaled cloud-native ML inference and analytics pipelines using AWS Lambda, S3, DynamoDB, Athena, SQS, and Step Functions, enabling reliable processing of large production datasets.
Optimized distributed processing workflows through algorithmic improvements, efficient data structures, and the elimination of redundant computation, reducing pipeline runtime from approximately 10 minutes to 1.5 minutes (85% improvement).
Automated large-scale evaluation and analytics workflows, reducing analysis turnaround time from 5 minutes to 30 seconds (90% improvement), while improving reproducibility and operational efficiency.
Designed partition-aware inference architectures that resolved distributed processing boundary conditions, improving prediction consistency and reliability across parallel workloads.
Built production-grade data analysis frameworks that processed and evaluated over 50,000 hours of real-world data, enabling large-scale trend analysis, correlation studies, and evidence-based decision support.
Developed scalable, asynchronous processing architectures supporting parallel execution of ML inference workloads.
Implemented memory and performance optimizations that improved resource utilization and reduced cloud processing costs.
Containerized critical workloads using Docker to improve deployment consistency, reproducibility, and operational reliability.

Machine Learning Engineer Intern

Datafoundry

01.2022 - 08.2022

Developed face recognition and motion detection solutions using MTCNN, FaceNet, TensorFlow, and OpenCV.
Built end-to-end preprocessing, inference, and classification pipelines using CNN and SVM models.

Education

B.Tech - Computer Science & Engineering

Amrita University

Coimbatore, India

01-2022

Skills

Machine Learning & AI: Deep Learning, CNNs, Feature Engineering, Model Evaluation, Inference Pipelines, Applied AI Systems

Data Engineering & Analytics: Data Pipelines, Distributed Processing, ETL, Workflow Automation, Performance Optimization, Data Modeling, Statistical Analysis

Cloud & Distributed Systems: AWS Lambda, S3, DynamoDB, Athena, SQS, Step Functions, Event-Driven Architecture, Serverless Computing

Data & Visualization: Pandas, NumPy, Athena Analytics, Automated Reporting, Research Data Analysis

Infrastructure & DevOps: Docker, Git, CI/CD, Production Deployment, Monitoring, Pipeline Reliability

Languages: Python, SQL

ACHIEVEMENTS

Cisco Innovative Idea Award (2019)

Science Innovation Award – DST India (2018)

Smart India Hackathon – First Round Qualifier (2020)

Timeline