DevOps Engineer II
deloitte
Job Description
- Embrace and drive a culture of accountability for reliability, performance, and cost outcomes, measured in service-level objectives and error budgets, not raw uptime. Operate the products, platforms, and environments you support to meet their SLOs within budget, and track incident trends and toil to prioritize the work that most improves reliability—ensuring high-quality, lean operational designs that keep production safe and resilient.
- Serve as the technical advocate for production reliability and operability, ensuring systems are admissible, performant, safe to run, and able to degrade gracefully when failure occurs. Uphold production standards, participate in the design of observability, performance and resilience testing, and operational tooling, and support the admission of systems into production—gating release on error budgets and automated reliability checks, and owning the readiness verification, environment integrity, and operational support that follow.
- Maintain accountability for the operational integrity of production and pre-production environments, and for the production standards that systems are admitted against. Own SLOs and error budgets; build and operate production observability—codified, version-controlled dashboards and SLO-driven, actionable alerting that detects before impact, plus the feedback loop into engineering; run performance, ambient-noise, and chaos testing to verify readiness; and guard environments against drift. Stay hands-on, self-driven, and continuously learn new approaches, languages, and frameworks—operating as an infrastructure-focused engineer, not a tool operator. Create technical specifications, runbooks, and shared playbooks; lead blameless postmortems that turn incidents into learning and systemic fixes; write high-quality, supportable automation to ensure all reliability KPIs (availability, performance, and cost) are met or exceeded. Demonstrate collaborative skills to work effectively with diverse teams.
- Develop lean operational solutions through rapid, inexpensive experimentation to meet the reliability needs of the engineering teams and the business. Engage with those teams before, during, and after delivery, co-defining service-level objectives and operational readiness so the right safeguards are in place at the right time, without becoming a bottleneck to delivery.
- Adopt a mindset that favors action and evidence over extensive planning. Utilize a leaning-forward approach to navigate complexity and uncertainty, hardening reliability through incremental, measurable improvements—progressive resilience testing and SLO refinement—rather than big-bang interventions, and keeping operations supportable and maintainable.
- Work collaboratively with empowered, cross-functional partners: engineering, platform engineering, security and risk, data governance, and engineering leadership and architecture. Uphold production standards and integrate their constraints so that the reliable, performant, and compliant path is the operative path. Co-define service-level objectives with the teams you support, verify readiness, and support the admission decision into production—holding the segregation-of-duties line as a dedicated, embedded function while partnering with security and risk on the control objectives you enforce. Foster a collaborative environment that enhances team synergy and innovation.
- Possess expertise in site reliability and modern production engineering—cloud platform ownership, observability (metrics, tracing, logging), performance and capacity engineering, chaos engineering, and cloud/AI cost engineering—together with applied AI fluency to operate AI and agentic workloads reliably, including AI and Agentic SSDLC, delivering production operations with full automation from discovery to production to operations and all quality checks through the SSDLC lifecycle. Learn to be a role model, leveraging these techniques to optimize reliability, performance, and operational delivery. Demonstrate understanding of the full lifecycle of platform and product development, focusing on continuous improvement and learning.
- Quickly acquire domain knowledge of the products and platforms you operate—and, where they are AI-infused, their distinct production failure modes such as drift, train/serve skew, latency and output variance, and token/GPU cost anomalies. Translate reliability needs, reference architectures, and operational requirements into service-level objectives, runbooks, and production tooling. Be a valuable, flexible, and dedicated team member, supportive of teammates, and focused on quality and tech debt payoff.
- Exhibit exceptional communication skills, capable of articulating complex technical concepts clearly and compellingly. Inspire and influence teammates and product teams through well-structured argume