SRE DevOps Engineer

Job description

Job Title: SRE DevOps Engineer Job Location: Bengaluru , Hyderabad & Chennai Key Responsibilities : Infrastructure Automation (Python-driven) • Develop and maintain Python scripts/tools to automate • Provisioning (VMs, containers, cloud resources) • Configuration management • System health checks and maintenance tasks • Build reusable automation frameworks and APIs • Reduce manual ops work through end-to-end automation pipelines • Create internal tools (Python-based) for dev productivity CI/CD Pipeline Engineering • End-to-end ownership of CI/CD pipelines, deployments, and production releases • Automate , Build, test, deployment workflows • Integrate Python automation for: Test orchestration & Deployment validations Site Reliability Engineering (SRE) Practices • Define and manage:SLIs, SLOs, SLAs • Improve system reliability, scalability, and uptime • Perform: Root Cause Analysis (RCA) &Incident management & postmortems Monitoring, Logging & Observability • Implement monitoring solutions: Prometheus, Grafana, ELK, Datadog, Splunk • Use Python for: Custom metrics collection , Log parsing and analytics & Alert automation • Support of ML platform operations and Kubernetes ecosystem Cloud & Infrastructure Management • Manage cloud platforms (AWS / Azure / GCP): Compute, storage, networking, serverless • Implement Infrastructure as Code (IaC): • Terraform, CloudFormation, ARM templates Experience & Mandatory skills: Overall 5 to 8 yrs exp with strong hold on Python Automation • Cloud: Microsoft Azure , IaC: Terraform • CI/CD: GitHub, GitHub Actions, Octopus Deploy • Containers & Orchestration: Kubernetes, AKS • MLOps: Kubeflow, KServe, Istio, EvidenceAI • Monitoring: ELK Stack, Prometheus, Grafana • Web/Hosting: IIS (Windows Server) • Database: SQL Server • Scripting: Python, Bash, PowerShell • End-to-end ownership of CI/CD pipelines, deployments, and production releases • Ownership of monitoring, alerting, and platform reliability • Responsibility for cloud infrastructure lifecycle management • Active contribution to platform modernization and migration initiatives • Support of ML platform operations and Kubernetes ecosystem • Direct involvement in customer onboarding and production support