Teja Emandi

Cloud Platform Engineer with 5 years of experience building and operating production-grade cloud infrastructure, Kubernetes platforms, CI/CD systems, and automation across GCP, Azure, and AWS. Strong hands-on experience with GKE/AKS, Terraform, Python, Helm, observability, cloud networking, security, and SRE practices. Currently focused on GPU-enabled Kubernetes and AI infrastructure operations, including workload scheduling, autoscaling, platform reliability, and infrastructure automation.

Experienced in improving deployment efficiency, production reliability, and operational scalability through automation and platform engineering practices.

Systems Engineer (Cloud DevOps) at Infosys IT Ltd (2021-11 – 2026-01)

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• Automated and managed multi-cloud infrastructure across GCP, AWS, and Azure using Terraform and Ansible, reducing provisioning effort by 40%.
• Designed and optimized CI/CD pipelines with Jenkins, Azure DevOps, and Bitbucket Pipelines, integrating automated testing, SonarQube validation, security scanning, and deployment workflows.
• Managed production GKE Kubernetes environments and containerized microservices using Docker and Helm, implementing HPA, rolling deployments, resource requests/limits, and workload optimization for high availability.
• Provisioned and secured cloud platforms using GCP, AWS, and Azure services, implementing HashiCorp Vault, Cloud Armor WAF, IAM controls, SSL/TLS, and firewall policies.
• Built enterprise observability solutions using Prometheus, Grafana, ELK, Cloud Monitoring, CloudWatch, and Azure Monitor, enabling proactive alerting and faster incident resolution.
• Improved application performance through Apache Solr tuning, Redis caching, Varnish optimization, and Akamai CDN integration, while automating operational workflows using Python and Shell scripting.
• Collaborated with Development, SRE, QA, and Platform teams to drive GitOps adoption, production troubleshooting, RCA, PIR activities, and continuous reliability improvements.

Senior Cloud & AIOps Analyst at Accenture (2026-04 – Present)

• Engineered and operated GPU-enabled AI infrastructure on GCP and Azure for production AI/ML workloads, focusing on Kubernetes scalability, workload scheduling, resource allocation, and platform reliability.
• Managed GKE/AKS GPU node pools with workload scheduling, autoscaling, resource optimization, ingress, and cluster reliability for production AI/ML workloads.
• Built Python-driven InfraOps automation for environment provisioning, validation, health checks, and operational workflows, reducing manual operations.
• Automated AI platform delivery pipelines using Azure DevOps, YAML, Terraform, and Git, enabling secure infrastructure deployments and repeatable releases.
• Implemented enterprise-grade observability and monitoring using Prometheus, Grafana, Cloud Monitoring, Azure Monitor, and Cloud Logging for Kubernetes, GPU utilization, and platform health.
• Secured AI cloud environments through IAM, service accounts, Key Vault, Cloud Armor, firewall policies, VPC/VNet networking, and compliance controls.
• Collaborated with AI engineers, data scientists, cloud architects, and platform teams to troubleshoot production issues, perform root cause analysis (RCA), optimize Kubernetes workloads, and continuously improve the reliability, scalability, and operational efficiency of AI platforms.

Master of Computer Applications in MCA – Vignan's Foundation for Science, Technology & Research (2022 – 2024)