• Led the migration of PayPal’s Secret Management Platform (KeyMaker) from a legacy Java microservices stack to a modern Golang + Vault-based architecture, improving secret retrieval performance by 40% and enhancing security posture.

• Designed and implemented Vault-integrated microservices with Golang, providing secure token-based authentication, automated secret rotation, and role-based access control (RBAC) for 200+ internal applications.

• Developed REST and gRPC APIs to streamline key lifecycle management—including creation, rotation, sandbox-toprod promotion, and smoke testing—resulting in 30% faster key onboarding.

• Integrated the platform with AWS EKS, Jenkins CI/CD, Prometheus, and Splunk to enable automated deployments, real-time monitoring , while implementing structured logging, metrics dashboards, and alerting pipelines that improved incident response time for SRE teams.

• Collaborated with cross-functional teams to refactor and modularize KeyMaker (Web, API, Domain, DAL layers), improving maintainability, test coverage, and supporting the transition to Vault-based secret storage.

• Mentored junior engineers on Golang best practices, secure coding, and system design, strengthening team capabilities.

• Developed v2 of Walmart App’s Post-Purchase operations backend using Golang, significantly improving system performance, reducing latency by 37% and maintainability while handling high-volume requests (5000+ TPS) from multiple upstream services.

• Designed and developed the Antipatterns microservice from scratch, integrating with Inventory, Dependency, and SRE Agent services to manage server-side technical dependencies across 25+ antipattern categories and 7 data platforms.

• Implemented efficient inter-service communication using gRPC and Protobuf, leveraging Go’s concurrency features (goroutines and channels) to optimize performance in both Antipatterns and post-purchase backend services.

• Developed a custom gating API using Gin framework helps in preventing faulty rollouts for application deployment control, and automated key insights dashboard updates, supporting proactive decision-making.

• Orchestrated containerized applications using Docker and Kubernetes on AWS EKS, implemented CI/CD pipelines with Jenkins, and integrated Akeyless and Vault for robust secrets management, enhancing system security, scalability and deployment efficiency.

• Designed and implemented an event-driven Chaos Testing (Monkey Testing) framework to validate Redshift cluster stability before each bi-weekly patch release, reducing post-release incidents by 30%, boosting cluster reliability.

• Orchestrated serverless workflows using AWS EventBridge, Step Functions, and Golang Lambdas to automate chaos test execution, log analysis, and ticket creation, improving testing efficiency and SRE response time by 25%.

• Developed a microservice-based Query Bank Tool that maintains metadata of all Redshift validation queries, enabling developers to reuse and auto-suggest queries for new feature testing and reducing duplicate validation queries by 60%.

• Integrated structured logging, metrics dashboards, and alerting pipelines with Splunk and internal monitoring tools to improve cluster observability and early anomaly detection.

• Collaborated with cross-functional Redshift teams to streamline release validation and feature testing, resulting in 40% faster pre-release validation cycles.

• Automated infrastructure provisioning and CI/CD pipelines using Terraform, Jenkins, and internal AWS pipelines, supporting multi-environment deployments (Dev, Test, Prod) and fleet-wide cluster monitoring.

• Designed and developed a C++ library for training massive neural networks on distributed systems using MPI, enabling efficient parallelization across multiple nodes.

• Achieved near-linear speedup on 793 cores of a 144 TFLOPS supercomputer, validating scalability and performance of distributed ML training.

• Conducted extensive experiments with MNIST dataset, optimizing hyperparameters and achieving 95.6% classification accuracy.

• Successfully integrated the library into 40+ projects, enabling large-scale distributed machine learning adoption.

• Implemented and deployed multiple machine learning algorithms (linear regression, decision trees, random forests, SVMs) to solve real-world problems and extract actionable insights.

• Contributed as part of a 7-member team to build a web-based doctor’s appointment scheduling platform, incorporating location and specialization-based search for efficient booking.

• Implemented Struts MVC and Front Controller patterns, developing Action Class components and configuring Struts XML files to streamline request/response handling.

• Integrated Spring Security and OAuth2 for secure user authentication and authorization.

• Built database layer using Spring JDBC, leveraging JDBC templates, row mappers, and bootstrapping for seamless data integration.

• Developed features including form validation (JavaScript), Servlets for business logic, and XML parsing techniques to ensure robust functionality.

• Enhanced the UI with JSP, JSTL, JavaScript, HTML, CSS, jQuery, and AJAX, ensuring a smooth user experience.

• Debugged and optimized Business Objects and Data Access Objects, improving system reliability.

• Performed unit testing (JUnit) and integration testing to validate application quality and scalability.