Work Experience

• Architected a centralized CI/CD observability platform on Grafana and Loki, aggregating pipeline telemetry from 100+ GitLab repositories to cut failure-investigation time across teams.

• Built real-time CI telemetry pipelines in Python against the GitLab API, running ~96 scheduled executions per day and streaming structured logs into centralized logging infrastructure.

• Engineered pipeline flakiness detection by correlating repeated runs per commit SHA, and layered AI-driven diagnostics on top for log summarization, failure classification, and root-cause analysis.

• Drove adoption of GitLab merge queues and CI gating policies across 50+ repositories, keeping shared branches stable under concurrent development.

• Automate platform infrastructure with Go, Terraform, Ansible, and VMware vSphere, including scheduled enforcement of VM resource-allocation policies.

• Automated build, test, and deployment workflows with GitHub Actions CI/CD pipelines.

• Set up weekly dependency-audit workflows that improved system stability and reduced security vulnerabilities.

• Containerized applications with Docker for consistent environments from development through production.

• Supported secure, scalable infrastructure with Node.js, Python, REST APIs, and OWASP practices.

• Scaled GCP infrastructure for production and AI-driven workloads, improving reliability and cutting latency by 25%.

• Optimized resource provisioning with Terraform and Kubernetes, lowering infrastructure costs and accelerating deployments.

• Presented infrastructure recommendations that shaped three executive-level decisions on GCP architecture and resource allocation.

• Provisioned AWS infrastructure with Terraform and CloudFormation, reducing environment drift between development and staging.

• Implemented GitHub Actions CI/CD pipelines for automated testing and releases on a two-week sprint cycle.

• Refactored backend APIs, cutting system response time by 45% for 200+ users.

• Built and deployed an AI-powered resume chatbot with Django and NLP, improving candidate success rates by 40%, with CI/CD workflows for automated deployment.

• Developed ML pipelines on AWS processing 10,000+ emails, improving customer satisfaction by 20%.

• Automated cloud security workflows for IAM, WAF, and access-control policies, strengthening security posture and deployment consistency.