Description
- 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.
- Established robust Kubernetes cluster monitoring and log aggregation through Grafana Agent and Grafana Cloud, allowing remote observability into customer installations.
- Built CI pipelines on GitHub Actions to automate building and testing of container images upon update to the product code.
- Automated end-to-end testing using PyTest, effectively saving over 4 hours of manual testing time per sprint.
- Developed a multi-stage ML pipeline to extract tabular data from XRay report images, thereby automating a manual step in the radiologist’s workflow.
- Strategically optimized the product’s infrastructure on AWS, reducing the cloud cost by over 50%.
- Actively engaged with potential customers as the lead developer, providing technical guidance and support to drive customer success.
- Revamped the entire product codebase, improving reliability and readability while fixing numerous production bugs to ensure the smooth operation of the product on customer sites.
