Developed MCP servers connecting LLMs with internal platforms such as Datadog to analyze infrastructure usage and operational metrics. Built AI agents that accelerated development and maintenance of internal automation tools. Designed, implemented, and maintained CI/CD pipelines using GitHub Actions, Bash, and Python, reducing manual deployment effort across multiple engineering teams. Supported more than 15 software engineering teams by troubleshooting and unblocking CI/CD pipelines to maintain continuous delivery. Developed and maintained custom containerized GitHub Actions runners, managing dependency versioning, security hardening, and image publishing to GCP Artifact Registry. Provisioned and maintained cloud infrastructure on Google Cloud Platform, including GKE and Cloud SQL, supporting over 10 services and workloads. Led SonarQube upgrades and implemented SonarScanner CLI integration across GitHub runners, improving code scanning capabilities, code quality and strengthening SDLC practices in the organization. Built and deployed macOS GitHub Actions runners on MacStadium Orka3 using Ansible, Packer, and Python, supporting 10+ iOS engineering teams and mobile CI pipelines. Implemented monitoring and alerting for CI/CD systems and ArgoCD deployments using Datadog dashboards, synthetic tests, event monitoring, and API integrations. Reduced development cloud spending by approximately 15% through infrastructure utilization analysis with Google Cloud Monitoring and Datadog. Enforced governance standards for CI/CD composite actions, dependency management, and vulnerability remediation across internal DevOps tooling. Developed GitHub security and compliance automation tools to promote organization-wide adoption of self-hosted runners, strengthening CI/CD security, reducing costs, and improving delivery speed. Enforced OIDC authentication across more than 1,000 GitHub Actions pipelines, improving security and eliminating long-lived credentials. Increased Go application test coverage from 40% to 90%. Contributed to Terraform, Ansible, Docker, and Helm-based infrastructure automation.

Developed a Python and Flask backend API for an agent-based simulation platform that generated synthetic Electronic Medical Records (EMRs). Built and deployed containerized applications using Docker, Amazon ECR, and Amazon EKS, leveraging Kubernetes ReplicaSets for scalable data generation workloads. Designed and managed PostgreSQL databases on local environments and Amazon RDS, optimizing performance for large-scale synthetic healthcare datasets. Generated and processed synthetic datasets representing approximately 500,000 patients to support healthcare modeling and simulation research. Developed React-based frontend interfaces to visualize simulation outputs and improve usability for researchers. Provisioned and maintained AWS infrastructure using EC2 initially and later migrating to the usage of Kubernetes pods and deployments in EKS for container orchestration, RDS for managed databases, S3 for object storage, VPC for network isolation, and IAM and Security Groups for access and security controls, supporting secure large-scale simulation workloads. Designed CI/CD pipelines using GitHub Actions to automate application build and deployment. Standardized cloud infrastructure through Terraform, improving infrastructure consistency and maintainability. Monitored system usage with Amazon CloudWatch and implemented automation with AWS Lambda to optimize resource utilization and reduce operational costs. Managed persistent storage for compute-intensive workloads using Amazon EBS volumes.