Deployment & DevOps Engineer deploying BMI’s wireless and software solutions. Managing Kubernetes, CI/CD, Linux, networking, and customer integrations.
About the role
- Site Reliability Engineer enhancing incident response and engineering practices for Vista's reliability. Focused on identifying failure patterns and implementing proactive improvements for operational excellence.
Responsibilities
- identify patterns of failure across the organisation.
- analyse incidents and post-incident reviews to find the recurring technical root causes behind customer impact, rather than treating each incident as a one-off.
- prioritise the biggest improvement levers.
- focus reliability effort where it most reduces Mean Time to Detect and Mean Time to Resolve, and where it proactively prevents the next incident from happening at all.
- turn those patterns into the right engineering intervention and influence the teams who can build it.
- help teams hands-on, in their code , through Merge Requests, pairing, code review, and active technical support, favouring the simplest intervention that prevents recurrence over the most elaborate one.
- disseminate and evangelise improvements across the organisation .
- lead the technical conversation in post-incident reviews and operational forums.
- help the Incident Response Team grow its engineering practice by pairing on real work, sharing what good engineering looks like in our context, and running internal learning sessions that bring the team from incident-response specialists toward incident-response engineers.
- partner across teams without direct authority.
Requirements
- 5 or more years of hands-on Site Reliability, Platform, or Infrastructure Engineering experience in a large-scale, distributed production environment, with proficiency in at least one programming language (e.g., Python, Go, TypeScript, Java)
- demonstrated experience driving adoption of a reliability or platform pattern (e.g., progressive delivery, observability standard, resilience library, secret rotation) across teams that did not report to you, with measurable outcomes.
- strong systems thinking and a demonstrable bias toward simple solutions - able to read an incident or a design and identify the underlying class of problem (retries, cascading failures, queueing behaviour, partial failures, head-of-line blocking) and the smallest, cheapest intervention that addresses it.
- comfortable choosing a post-deploy curl check over a full sandbox environment when the simpler intervention would prevent the same incident.
- hands-on experience with the modern reliability stack: at least one major cloud platform (AWS, Google Cloud, or Azure), an observability platform (for example New Relic, Datadog, or Grafana), defining and operating against Service Level Objectives, continuous integration and deployment pipelines, and infrastructure-as-code (for example AWS CDK, Pulumi).
- hands-on exposure to Artificial Intelligence and Large Language Model tooling in an engineering context, for example integrating Large Language Models into workflows or operational tooling, or using Artificial Intelligence meaningfully in your own engineering.
Benefits
- health, wealth and wellness programs
- long-term equity incentives
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