SRE Engineer
Sourceo6–8 yrsMumbaiOn-siteDirect career pageEst. 20–35 LPA
Posted on 9 Sept
Reach people at Sourceo
People at this company you can reach
Log in to see who works here
Create a free account or log in to see the people at Sourceo you could write to:
- Recruiters & HR
- People in this function
- Work addresses
Your match
Against your primary resume
Log in to see your match score
Create a free account or log in to unlock your CV match score across:
- Role fit
- Experience
- Industry relevance
Protocol Intelligence
How competitive this role is
Competition
Background fit
Strictness
Log in to see why each signal reads the way it does.
Job description
Structured overview of role & requirements
About This Role
- Define, monitor, and enforce Service Level Objectives (SLOs), Service Level Indicators (SLIs), and Error Budgets specifically for AI services.
- Maintain and operate scalable, fault-tolerant cloud infrastructure (Azure) including Kubernetes clusters and containerized AI microservices with IaC tools like Terraform.
- Lead incident response, root cause analysis, proactive reliability risk mitigation, and build automation for operational tasks including CI/CD pipelines and intelligent alerting systems.
Minimum Requirements
- 6-8 years of relevant work experience in supporting production-grade, high-availability systems.
- Bachelor’s degree in Computer Science, Information Technology, or a related field.
- Strong proficiency with Azure cloud services (AKS, Azure DevOps, ARM) and Infrastructure as Code tools such as Terraform.
- Experience with containerization (Docker), orchestration (Kubernetes), CI/CD tools, and maintaining observability stacks using Prometheus, Grafana, Datadog, or Open Telemetry.
Ideal Candidate Profile
- Experienced in managing multi-tenant, microservices-based architectures with a focus on AI/ML model serving and data workflows.
- Demonstrates a reliability-first mindset with hands-on leadership in incident management and production stability for AI services.
- Skilled in automation and MLOps practices, partnering effectively with data engineers and data scientists to operationalize AI systems securely and scalably.