Site Reliability Engineer

2 days ago

Pune, IndiaMid Level / Senior

Responsibilities

Design and implement scalable, highly available cloud infrastructure for production workloads.
Automate infrastructure provisioning, scaling, and lifecycle management with Infrastructure-as-Code.
Lead reliability improvements across distributed cloud-native systems and define SLI, SLO, SLA, and reliability standards.
Manage CI/CD pipelines, automated testing, blue-green deployments, and canary releases.
Optimize Kubernetes environments for availability, scalability, performance, and cost efficiency.
Implement observability covering monitoring, distributed tracing, logging, and alerting.
Troubleshoot large-scale reliability, latency, performance, and scalability issues.
Operate event-driven architectures and messaging systems such as Kafka and RabbitMQ.
Implement cloud security, identity and access control, and infrastructure governance practices.
Drive incident management, root-cause analysis, capacity planning, performance optimization, and reliability forecasting.
Apply AI technologies to observability, operational workflows, runbook generation, and troubleshooting.
Mentor junior engineers and partner with platform, security, and engineering teams to improve resilience and operational excellence.

3–5 years of experience in Site Reliability Engineering, DevOps, Cloud Engineering, or Platform Engineering.
Experience designing and operating scalable distributed systems in cloud environments.
Strong expertise with Azure, AWS, GCP, or hybrid-cloud platforms.
Deep knowledge of Terraform, Pulumi, CloudFormation, Bicep, or equivalent Infrastructure-as-Code tooling.
Strong Kubernetes and container orchestration experience.
Experience operating and optimizing CI/CD and deployment automation platforms.
Expertise with observability platforms such as Dynatrace, Datadog, Prometheus, Grafana, OpenTelemetry, or New Relic.
Understanding of SLOs, SLIs, SLAs, error budgets, and incident management.
Experience with messaging systems and event-driven architectures.
Knowledge of networking, cloud security, identity management, and automation practices.
Ability to troubleshoot performance, scalability, and reliability issues at scale.
Experience using AI-assisted solutions for observability, automation, operational analytics, and runbook generation.
Strong communication, collaboration, and mentoring skills.
Preferred: cloud certifications, Kubernetes certifications such as CKA or CKAD, FinOps and cloud cost optimization experience, mission-critical enterprise or SaaS platform experience, and exposure to AIOps, predictive monitoring, or AI-enhanced operations platforms.