Sandeep Thontla

<p>Worked on DevOps activities for the Deep Health project with focus on GitLab-based source control/CI workflows, Terragrunt-managed infrastructure, Argo CD deployments, and GCP cloud environments.<br>
Supported GitLab-based repository workflows and CI/CD activities for application delivery and environment updates.<br>
Worked with Terragrunt to manage and organize Terraform-based infrastructure changes across GCP environments.<br>
Used Argo CD for GitOps-based Kubernetes deployment tracking, synchronization, and rollout verification.<br>
Assisted with GCP cloud environment operations, deployment validation, and coordination with platform/application teams.<br>
Engineered AI-assisted GitLab CI/CD pipelines incorporating automated code analysis to validate configurations and optimize build artifact caching.<br>
Implemented AI-driven Argo CD GitOps workflows to detect deployment anomalies in GCP environments and trigger automated rollback actions.</p>

<p>Worked on multi-cloud infrastructure automation, CI/CD modernization, Kubernetes administration, cloud security, monitoring, incident response, and SRE practices across AWS and Azure environments.<br>
Designed and maintained multi-stage Azure DevOps YAML pipelines for microservices, integrating automated testing, SonarQube quality gates, and zero-downtime deployment strategies.<br>
Automated multi-region Azure resource provisioning (VMs, VNets, NSGs, VPN gateways, and scale sets) using modular Terraform configurations and ARM templates to ensure high availability.<br>
Administered and scaled Azure Kubernetes Service (AKS) clusters, managing node pools, RBAC, container lifecycle, and secure microservice deployments.<br>
Streamlined continuous delivery by pairing Azure DevOps and Jenkins workflows with automated containerization and cloud security controls.<br>
Configured and managed Azure Active Directory (AAD), including user/group administration, RBAC assignments, and conditional access policies.<br>
Oversaw Azure Backup and Recovery Services, implementing retention policies and disaster recovery drills for business continuity.<br>
Created Python and PowerShell automation for provisioning, compliance reporting, budget automation, IAM group management, and security operations.<br>
Implemented observability using CloudWatch Synthetics, Azure Monitoring, Grafana dashboards, and custom alerting.<br>
Supported cloud DNS/routing, multi-region failover, automated traffic recovery, incident response, RCA, and blameless postmortems.<br>
Automated Azure DevOps and Jenkins build pipeline generation using AI scripts to dynamically create YAML configurations based on repository code structures.<br>
Deployed AI-enhanced monitoring and log analytics on AKS clusters to predict pod resource consumption and execute intelligent microservice deployments.</p>

<p>Led DevOps and cost-effective work for high-availability cloud platforms, IaC automation, reusable CI/CD workflows, cloud migration, monitoring, and operational excellence.<br>
Authored reusable Terraform and Ansible templates to automate cloud infrastructure across Azure subscriptions, reducing deployment overhead and enforcing governance policies.<br>
Architected standardized Azure DevOps and Jenkins pipeline templates integrated with automated testing suites and security scanning to improve deployment velocity.<br>
Designed and maintained high-availability architecture on Azure networking, storage (Blob, File, Disk), and load balancing services with lifecycle management.<br>
Configured Azure Monitoring and Log Analytics workspaces to centralize metrics, alerts, and diagnostic logs for proactive issue resolution.<br>
Designed and orchestrated AKS and OpenShift clusters for microservices workloads, integrating monitoring with Prometheus/Grafana and enforcing SRE controls.<br>
Supported on-prem/application migration to AWS and Azure using migration services, storage transfer, and IaC-first modernization.<br>
Established SRE controls such as runbooks, postmortem templates, escalation routines, access policies, and compliance processes.<br>
Integrated AI-driven static code and dependency analysis tools into CI/CD build pipelines to accelerate automated security scanning and build optimization.<br>
Orchestrated AI-informed pod autoscaling and automated deployment validation across AKS and OpenShift clusters, significantly minimizing operational downtime.</p>

<p>Supported build/release engineering, CI/CD, migration from legacy tools, cloud infrastructure automation, Kubernetes operations, monitoring, and reliability practices.<br>
Developed Terraform and CloudFormation modules to automate core Azure and AWS networking, compute, and security stack provisioning for hybrid cloud environments.<br>
Built end-to-end build and release pipelines in Azure DevOps and Jenkins, streamlining application delivery for .NET, Java, and Node.js microservices.<br>
Led modern CI/CD adoption by executing migrations from legacy TFS and VSTS setups into Git-based Azure DevOps pipelines and package management workflows.<br>
Deployed and monitored Kubernetes clusters across AKS, GKE, and EKS with automated scaling, centralized logging, and proactive alerting for production workloads.<br>
Built centralized logging and alerting using ELK, Grafana, Prometheus, and CloudWatch for SRE visibility.<br>
Automated deployments, patching, monitoring, resource management, and deployment script generation using Python automation and early AI scripting tools.<br>
Handled incident response, RCA, postmortems, disaster recovery support, environment consistency, and technical onboarding.<br>
Leveraged intelligent script generation to automate complex application deployment steps across hybrid Azure and AWS environments.<br>
Implemented machine-assisted log anomaly detection within ELK and CloudWatch to streamline Kubernetes release monitoring and issue resolution.</p>