Applied AI SRE III - PxE GPS

Applied AI Site Reliability Engineer III Role Overview: As an Applied AI Site Reliability Engineer III , you will actively engage in your engineering craft, taking a hands-on approach to the reliability, performance, and operational integrity of high-visibility products and platforms and the environments they run in. Your expertise will be pivotal in keeping production safe, performant, and cost-effective, while driving tangible value for Deloitte’s engineering investments. You will leverage your extensive engineering craftsmanship across cloud platform engineering, observability, and performance and reliability engineering—together with applied AI fluency that lets you reliably operate AI and agentic workloads alongside the rest of the portfolio—consistently demonstrating your strong track record in operating high-quality, resilient systems at scale. The ideal candidate will be a dependable team player, collaborating with cross-functional teams to uphold production standards, safeguard environments, and admit systems into production with confidence.

Outcome-Driven Accountability: 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. Technical Leadership and Advocacy: 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, lead the design of observability, performance and resilience testing, and operational tooling, and own 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. Engineering Craftsmanship: 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. Customer-Centric Engineering: 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. Incremental and Iterative 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. Cross-Functional Collaboration