Jan 2018 – Feb 2019 · 1 yr 1 mo · On-site

• ECG Signal Processing Android App:
• Architected and developed an end-to-end Android application for ECG signal processing, leveraging computer vision models and machine learning techniques. The application seamlessly integrates backend services, Android client development, and a robust DevOps pipeline for continuous integration and deployment.
• Key Features:
• Architecting and implementing backend services for data management and analysis.
• Developing Android client for user interaction and seamless integration with backend services.
• Utilizing computer vision models for raw ECG report conversion to digital signals.
• Implementing Denoising Autoencoders for noise reduction in ECG signals.
• Deploying Deep Learning models for heart arrhythmia prediction.
• Establishing a comprehensive ML Ops pipeline for efficient machine learning model deployment and management.
• Source: https://timesofindia.indiatimes.com/india/iit-roorkee-develops-dhadkan-mobile-app-for-saving-lives-of-heart-failure-patients/articleshow/63986333.cms
• Preeclampsia Prediction Android App:
• Architected and developed an end-to-end Android application for predicting preeclampsia in pregnant women, encompassing backend development, Android client development, and advanced ML deployment techniques.
• Key Features:
• Designing and implementing backend infrastructure for data management and analysis.
• Developing Android client application for seamless user interaction.
• Deploying ensemble machine learning algorithms on real data from AIIMS for preeclampsia prediction.
• Automated notifications to patients and doctors using ML Ops techniques upon emergency detection.
• In-app medicine reminders with real-time WHO ANC level test analysis.
• Establishing a robust DevOps pipeline for continuous integration and deployment of machine learning models.
• Impact:
• The application aids in early detection and proactive management of preeclampsia, contributing to improved maternal and fetal health outcomes.

Aug 2017 – Feb 2019 · 1 yr 6 mos · Gurugram · Hybrid

• Led the development of a web-based analytics engine for solar data, driving plant optimization and performance enhancement towards net-zero emissions.
• Key Achievements:
• Analyzed kWh production, efficiency metrics, and degradation rates using PySpark, boosting accuracy.
• Reduced downtime by up to 15% and increased energy yield by 5% with machine learning.
• Achieved a 20% reduction in revenue loss by identifying downtime instances in kWh hours.
• Developed ML forecasting models with 90% accuracy for short and medium-term energy forecasts.
• Optimized energy generation in kWh hours by 25% through dynamic generation scheduling.
• Provided actionable insights via a user-friendly Solar Analytics Dashboard.
• This analytics engine significantly contributes to reducing carbon emissions, aligning with our commitment to achieving net-zero emissions.

Jul 2016 – Aug 2017 · 1 yr 1 mo · Gurgaon, India · On-site

• I played a key role in developing Survey2connect (S2C), a leading SaaS experience management platform. Our platform transforms feedback into actionable insights, serving 200+ clients globally.
• Key Contributions:
• Developed core modules: Survey Simplified, Customer Experience, Employee Satisfaction, Brand Tracking & Awareness, Communities, and Mystery Shopping solutions.
• Helped shape platform architecture and frameworks.
• Impactful Metrics:
• 🎯 200+ Clients Served
• 📋 1 Mn+ Surveys Deployed
• 📈 1 Bn+ Responses Generated
• 🔄 100 Mn+ Workflows Created
• Learn More:
• Discover how Survey2connect is shaping experience management: https://www.xebo.ai/

Nov 2015 – Aug 2017 · 1 yr 9 mos · Gurgaon, India