- CI/CD TOOLS: Jenkins, GitHub Actions, Travis CI, GitLab, Circle CI
- BUILD TOOLS: Maven, Gradle
- CONTAINERIZATION TOOL: Docker, Podman
- SOURCE CODE MANAGEMENT: Git
- CONTAINER MANAGEMENT TOOL: Kubernetes
- LANGUAGES: Python, C++
- OPERATING SYSTEMS: Linux, RHEL, Ubuntu, Windows
- DATABASES: MySQL
- LIBRARIES: Numpy, Pandas, Matplotlib, Seaborn
- INFRASTRUCTURE AS CODE: Terraform, Ansible
- OTHERS: JIRA, Data Structures and Algorithms, ML
- M.Tech, Data Science | College of Engineering Pune (May 2025)
- B.E., Information Technology | Pune University (May 2021)
Senior Software Engineer @ Persistent Systems (Sep 2023 - Present)
- Developed and implemented a continuous integration /continuous deployment (CI/CD) pipelines using tools like Jenkins and GitLab, reducing project delivery times by 20%.
- Experience in standalone and openshift QE Validation of Apache Camel-quarkus, Camel-spring-boot, Camel-k.
- Created Python/Bash scripts to automate manual tasks.
- Hands-on experience in Kubernetes and Openshift cluster creation and management.
- Experienced in Multiarch Images creation using cross compilation tools like Buildx, Buildah on Github Actions CI.
- Successfully automated resource configuration using Ansible, reducing manual configuration errors by 30%.
- Utilized infrastructure-as-code tools like Terraform to provision and manage cloud resources.
- Successfully implemented ad-hoc request to utilize local Maven Artifacts built specifically for power to resolve product dependency.
Software Engineer @ Persistent System (Sep 2021 - Aug 2023)
- Ported various Open-Source projects to support on IBM PPC64LE architecture.
- Enabled Power support for Minikube lightweight kubernetes implementation which resolved major dependency for E2E testing of many packages.
- Worked on multi-staged Dockerfiles for multiarch Images publishing to various public and private registries resulting in reduction in test failures by 40%.
- Implemented and configured containerization technologies such as Docker and Kubernetes to enable scalable and portable application deployments.
Developed objective strategy for discovering optimal EEG bands based on signal power spectra using Python. This data-driven approach led to better characterization of the underlying power spectrum by identifying bands that outperformed the more commonly used band boundaries by a factor of two. The proposed method provides a fully automated and flexible approach to capturing key signal components and possibly discovering new indices of brain activity.
Used Matlab to train over 100 machine learning models which estimated particulate matter concentrations based on a suite of over 300 biometric variables. We found biometric variables can be used to accurately estimate particulate matter concentrations at ultra-fine spatial scales with high fidelity (r2 = 0.91) and that smaller particles are better estimated than larger ones. Inferring environmental conditions solely from biometric measurements allows us to disentangle key interactions between the environment and the body.
Bravo Team Award from PSL : To appreciate efforts towards building skills on Azure and contributions to Azure competency initiative.
- Microsoft Certified: Azure Data Fundamentals
- Microsoft Certified: Azure Fundamentals