Define meaningful SLIs, SLOs, and reliability targets for the platform.

Collaborate with the software engineering teams to define and achieve the best practices for software observability, SLIs, SLOs and reliability.

Strengthen production readiness by improving service ownership, observability, alerting, runbooks, scaling assumptions, rollback paths, and failure-mode preparedness.

Improve the reliability, scalability, and performance of cloud, Kubernetes, and shared infrastructure, including how systems scale during growth, traffic spikes, and dependency failures.

Build actionable observability using metrics, logs, traces, and golden signals, with tools such as Datadog, Prometheus, and Grafana.

Implement operational and security best practices through guidelines, policies and automation.

Reduce alert noise and improve signal quality so teams can detect, understand, and resolve issues quickly.

Automate repetitive operational work using Python or other languages, turning recurring manual work into safer automation and clearer runbooks.

Implement self-service Internal Developer Platform features via APIs and Kubernetes operators.

Improve deployment safety, rollbackability, and release observability.

Improve reliability of critical stateful systems such as databases, caches, queues, and streaming platforms.

Participate in on-call, troubleshoot, and coordinate incident response, and facilitate blameless post-incident reviews that turn into concrete improvements.

Execute disaster recovery drills and analyse cloud/platform usage to identify cost and resource-efficiency gains without compromising reliability.

You have 7+ years of production experience operating Kubernetes-based platforms and cloud infrastructure.

You understand and apply SRE practices: SLIs, SLOs, error budgets, production readiness, incident response, post-incident learning, toil reduction, scalability, capacity planning, high availability, backups, and disaster recovery.

You can design and improve observability and alerting for critical systems using metrics, logs, traces, and golden signals, and are comfortable troubleshooting complex distributed systems to identify systemic reliability improvements.

You can write maintainable software to automate operational tasks and reduce manual intervention.

You have experience with stateful production systems such as relational databases, caches, queues, or streaming platforms.

You know how to balance reliability, performance, cost, and delivery speed pragmatically.

You are comfortable working in a transitional environment where SRE practices are being introduced while critical infrastructure and delivery systems still need hands-on reliability support.

You collaborate effectively with Engineering, Platform, Security, and Product stakeholders.

You communicate clearly, document well, and enjoy coaching teams toward stronger production ownership.

You model initiative and accountability, raising risks early and driving improvements through to completion.

Critical services have clear owners, meaningful SLIs/SLOs, actionable alerts, dashboards, runbooks, and production readiness coverage.

Reliability targets are consistently met across critical infrastructure and services.

Operational toil and manual intervention are measurably reduced through automation and safer workflows.

MTTR improves through reduced alert noise, better signal quality, stronger observability, and clear incident response playbooks and escalation paths.

Post-incident actions are tracked, completed, and used to reduce repeat incidents.

Disaster recovery exercises validate that critical services and infrastructure can recover within agreed expectations.

Cloud and infrastructure resources are optimised without sacrificing performance, elasticity, or resilience.