SRE DevOps- Python Automation Engineer

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