Cloud/DevOps Engineer - Multi-Cloud / CI-CD Expert

SAI VARDHAN REDDY Cloud/ DevOps Engineer

West Haven, CT 203-***-**** **************@*****.*** LinkedIn SUMMARY

• Cloud/DevOps Engineer with 3.5+ years of experience delivering scalable, automated environments using Python, Bash, PowerShell, and YAML/JSON, leveraging Rego-based policy controls to streamline multi-cloud deployments and boost efficiency by 18%.

• Designed and optimized multi-cloud architectures across AWS, Azure, and GCP, using EC2/EKS, AKS/App Services, Lambda, API Gateway, and core VPC/Networking to support reliable, high-availability systems.

• Containerized and orchestrated enterprise workloads using Docker, Kubernetes (EKS/AKS/OpenShift), Helm, and Istio, enabling stable git-ops driven rollouts through ArgoCD/Flux and cutting deployment failures by 15%.

• Implemented CI/CD automation through Azure DevOps, GitHub Actions, Jenkins, and AWS CodePipeline/CodeBuild, improving release frequency from weekly to daily with strong guardrails and automated quality checks.

• Delivered infrastructure automation through Terraform, Ansible, CloudFormation, ARM, and Bicep, improving provisioning speed by 30% and strengthening environment consistency and compliance across teams.

• Built and tuned cloud-native data platforms using PostgreSQL, MySQL, MongoDB, Redis, Aurora, and DynamoDB, integrating scalable file storage like AWS S3, Azure EFS, and NFS for application workloads and analytics pipelines.

• Strengthened observability using Grafana, Datadog, CloudWatch, AZ Monitor, and ELK, implementing log analytics with KQL and establishing alerting standards that cut mean-time-to-detect by nearly 25%.

• Applied cloud security and compliance automation through AWS Config, Azure Policy, SIEM-integrated alerting, and Zero Trust controls, reducing misconfigurations and improving audit readiness across production environments.

• Collaborated using modern DevOps tools such as GitHub, GitLab, Bitbucket, Nexus, and JFrog Artifactory, maintaining clean branching standards and ensuring reliable artifact management for multi-team workloads.

• Engineered and deployed AI/ML workloads using SageMaker, Azure ML, and Vertex AI, supporting scalable training pipelines and accelerating model deployment cycles for data science teams by 20%. TECHNICAL SKILLS

Languages & Scripting: Python (including PySpark), Bash, PowerShell, YAML, JSON, Groovy. Rego Cloud Platforms: AWS (EC2, EKS, ECS, Lambda, S3, RDS, DynamoDB, Aurora, CloudFront, API Gateway, VPC, IAM, CloudWatch, CloudTrail, AWS Config, Security Groups, ElastiCache, Step Functions, MSK), Azure(AKS, App Services, Virtual Machines, Azure Functions, API Management, Azure SQL, Application Gateway, Load Balancer, Azure Firewall, Azure WAF, Recovery Services Vault), GCP (GKE - familiarity)

Infrastructure as Code (IaC): Terraform, Ansible, AWS CloudFormation, ARM Templates, Bicep, Azure CLI, AWS CDK Containerization:

Cloud AI / ML Services:

Kubernetes (EKS, AKS, OpenShift), Docker, Helm, Istio, GitOps (ArgoCD / Flux) AWS SageMaker, Azure Machine Learning, Google Vertex AI CI/CD & Automation: Azure DevOps, GitHub Actions, Jenkins, AWS CodePipeline, AWS CodeBuild, Bamboo, GitOps, Infrastructure Automation

Databases & Storage: PostgreSQL, MySQL, MongoDB, Aurora, DynamoDB, Redis, AWS S3, Azure EFS, NFS Monitoring, Logging: Grafana, Datadog, ELK Stack, CloudWatch, Azure Monitor, Log Analytics, KQL Security & Compliance:

DevOps & Version Control:

IAM, RBAC, Zero Trust, TLS/SSL, OAuth, OpenID Connect Security Groups, NSG, VPN, Encryption Standards, SIEM Integration Cloud Security Best Practices (AWS/Azure) GitHub, GitLab, Bitbucket, Nexus, JFrog Artifactory PROFESSIONAL EXPERIENCE

Cloud / DevOps Engineer

Accenture New York Feb 2025 – Current

• Built and automated scalable cloud workloads using Python, Bash, YAML, and Groovy, improving CI workflows by 18% while integrating IaC pipelines with Terraform and AWS CDK to streamline multi-account provisioning.

• Deployed and managed production-grade Kubernetes clusters (EKS, AKS) and Docker-based microservices, optimizing pod autoscaling and rolling strategy which reduced deployment rollbacks by nearly 12%.

• Engineered CI/CD pipelines using Azure DevOps, GitHub Actions, and Jenkins to standardize build, test, and release steps, cutting average release cycle time by 20% across key application teams.

• Designed secure AWS workloads across EC2, Lambda, S3, DynamoDB, Aurora, CloudFront and API Gateway with hardened IAM, VPC security groups, and CloudTrail-based audit rules for compliance readiness.

• Strengthened cloud security through IAM, RBAC, Zero Trust principles, TLS/SSL enforcement and Security Groups/NSGs, raising compliance posture based on quarterly internal checks and SIEM-integrated alerting.

• Developed automated observability dashboards using Grafana, Datadog, and ELK integrations, improving incident detection time and cutting mean-time-to-identify by almost 10%.

• Used AWS SageMaker and Azure ML pipelines to automate small-scale model training and inference jobs, integrating serverless execution patterns for experimentation workloads.

• Managed repository governance across GitHub and GitLab with automated branching rules, artifact versioning via Nexus and Art factory, and security scanning aligned with Zero Trust and encryption standards. Environment: Python, Bash, YAML, Groovy, Terraform, AWS CDK, AWS (EC2, Lambda, S3, DynamoDB, Aurora, CloudFront, API Gateway), Azure (AKS, Azure DevOps, Azure ML), Kubernetes (EKS, AKS), Docker, GitHub Actions, Jenkins, GitHub, GitLab, Grafana, Datadog, ELK, CloudTrail, IAM, RBAC, Security Groups, NSGs, TLS/SSL, AWS SageMaker. Cloud / DevOps Engineer (Intern)

Accenture New York Aug 2024 – Jan 2025

• Built containerized workloads using Docker, Kubernetes (AKS, EKS) and OpenShift, enabling stable release cycles and lowering container-related incidents by nearly 14% in quarterly audits.

• Created CI/CD pipelines with Jenkins, Bamboo and AWS CodePipeline to support microservice deployments, adding automated testing stages that improved release quality with measurable reduction in regressions.

• Deployed end-to-end AWS stacks leveraging EC2, S3, RDS, DynamoDB, MSK, CloudWatch and security-hardened IAM roles to meet performance and compliance benchmarks for internal platforms.

• Managed Azure SQL, Recovery Services Vault, Application Gateway, Load Balancer and Azure Firewall configurations to support HA/DR patterns aligned to enterprise reliability policies.

• Worked with GCP (GKE) for small-scale workload migration PoCs, validating autoscaling behavior, cost profiles and network configuration patterns across multi-cloud environments.

• Oversaw version control, artifact governance and secure code practices with GitHub, Bitbucket and JFrog Artifactory, while reinforcing RBAC, OAuth and encryption policies across all pipelines. Environment: Docker, Kubernetes (AKS, EKS), OpenShift, Jenkins, AWS CodePipeline, AWS (EC2, S3, RDS, DynamoDB, CloudWatch, IAM), Azure (SQL, Load Balancer, Firewall), GCP (GKE), GitHub, Bitbucket, JFrog Artifactory, RBAC, OAuth. Cloud Engineer

Mphasis India Jun 2021 - Jul 2023

• Built scalable workloads on AWS EC2, ECS, and EKS, Lambda and VPC architectures, improving service reliability during peak traffic and enabling smoother cross-region failovers supported by CloudWatch operational insights.

• Implemented distributed systems using Azure Virtual Machines, AKS, App Services, Azure SQL and Recovery Services Vault, reducing deployment friction and strengthening backup and recovery readiness for mission-critical apps.

• Automated cloud provisioning with Terraform, Ansible, CloudFormation and Bicep, establishing reusable templates that reduced provisioning time from hours to minutes while supporting multi-cloud governance models.

• Managed CI/CD pipelines in Azure DevOps, GitHub Actions and Jenkins, adding automated testing and security validation that cut release-related defects by around 15% and ensured consistent delivery across dev to prod.

• Optimized storage and DB layers with Aurora, PostgreSQL, MongoDB, Redis and Amazon S3, improving query performance and establishing lifecycle rules that dropped storage costs in monthly audits.

• Enhanced observability using Grafana, Datadog, ELK Stack and Azure Monitor, building dashboards and KQL-based queries that shortened triage time and helped identify recurring infra bottlenecks.

• Deployed container registries and artifact repositories through GitHub, GitLab, Bitbucket and JFrog Artifactory, creating controlled release pipelines and improving package traceability across teams.

• Built and tuned ML workloads using SageMaker, Azure ML, and early-stage work with Google Vertex AI, enabling data teams to operationalize model training pipelines with stable scheduling and monitoring. Environment: AWS (EC2, ECS, EKS, Lambda, VPC, CloudWatch, S3, Aurora), Azure (VMs, AKS, App Services, Azure SQL), Terraform, Ansible, CloudFormation, Bicep, Azure DevOps, GitHub Actions, Jenkins, PostgreSQL, MongoDB, Redis, GitHub, GitLab, Bitbucket, SageMaker, Azure ML, Google Vertex AI. EDUCATION

M.S in Information Technology

ST. Francis College New York Sep 2023 – May 2025 GPA: 3.60/4 B. Tech in Computer Science

Prathyusha Engineering College Chennai Jul 2019 – Jun 2023 CGPA: 8.00/10 CERTIFICATION

• Google Cloud Platform Fundamentals Udemy, 2025

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