Experiences

Description Building scalable backend infrastructure to transform the legal experience for all

Description Developed a multi-stage LLM pipeline using Llama3 to extract key information from X-Ray report images, leading to the creation of a new product in our suite. Architected and developed a cloud-native version of the product on Kubernetes and packaged it as a Helm chart for ease of deployment, distribution and versioning. Enhanced operational efficiency by implementing GitOps at scale using ArgoCD, enabling centralized management of multiple K8s clusters, cutting down the release and software update timings by a magnitude....

Description Developed an event-driven serverless integration pipeline using AWS Lambda and EventBridge, facilitating seamless bidirectional synchronization of customer data between Salesforce and Boomerang, thereby enhancing customer onboarding experience. Developed a Slack bot that sent interactive daily notifications to customers, facilitating direct actions such as task assignments and completions from within Slack. This streamlined operations and boosted sales by over 50%. Engineered a job tracking system to monitor asynchronous data transfer jobs via AWS AppFlow....

🔗 GitHub Description Guide: Mohammad Farid Azampour (Visiting Researcher at Chair for Computer Aided Medical Procedures, TU Munich) Rearchitected Pix2Pix, a CGAN architecture, to generate Ultrasound (US) scans from MRI scans Addressed the challenge of structural dissimilarity between MRI and US scans due to data collection methods Developed a custom loss function combining CGAN loss and Dice Loss for improved image segmentation Implemented the loss function to encourage the generator to eliminate structural deformation in the generated US scans Leveraged remote access to TU-Munich’s cluster computers for fast model training Utilized TU-Munich’s Discourse forum for collaboration which drove the project to fruition

Description Guide: Dr. Sripad Krishna Devalla (co-founder and CTO at OriginHealth) Developed a configuration-driven framework with data preprocessing pipeline for training deep learning models on AWS EC2 instances Standardized the training procedure for ML models, saving over 100 hours of development time for the ML team Worked on fetal head segmentation using the developed framework Gained insights into the applications of AI and Computer Vision in medical diagnosis Acquired experience in working with limited and sensitive healthcare data

🔗 GitHub Description Guide: Prof. Dr. Pratyush Kumar (Assistant Professor, Dept. of Computer Science, IIT Madras) Implemented a Convolutional Neural Network (CNN) for 6-DoF Global Pose Regression and Odometry Estimation Developed a deep learning model in TensorFlow to estimate the camera pose from consecutive monocular images The model demonstrated superior performance compared to traditional feature-based visual localization algorithms, especially in texture-less regions The neural network was later utilized for robot localization in GPS-denied environments