Lead Applied AI Site Reliability Engineer II - PxE ServiceNow

Deloitte•Nashville, TN

23h•$113,100 - $208,300•Hybrid

About The Position

As a ServiceNow Lead Applied AI Site Reliability Engineer II, 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 and advanced proficiency 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 exemplary track record in operating high-quality, resilient systems at scale. The ideal candidate will be a role-model leader and mentor, collaborating with cross-functional teams to set production standards, safeguard environments, and admit systems into production with confidence.

Requirements

Bachelor’s degree in computer science, software engineering, data science, machine learning, or related discipline.
6+ years of software engineering and site reliability engineering experience operating large-scale, distributed, cloud-native systems in production.
Proficiency in most of the following: Python, Go, Bash, Java, C#/.NET, SQL/NoSQL, Kubernetes, Terraform, ArgoCD, as well as CI/CD and observability stacks.
3+ years of experience in site reliability or production engineering for large-scale systems including defining and owning SLIs, SLOs, SLAs, error budgets, incident command and on-call, and production observability.
3+ years of experience with cloud-native engineering and cloud platform ownership on Azure, AWS, or GCP.
1+ years of experience establishing reliability and operational standards including actively leading, mentoring, and guiding team members.
Prior experience operating AI/ML and agentic workloads in production and their reliability failure modes.
Prior experience with load and performance testing, chaos engineering, capacity planning, autoscaling, and cloud/AI cost engineering.
Prior software engineering experience with understanding of BCD, sequence/activity/state/entity relationship/data flow diagrams, OOP/OOD, data structures, algorithms, and code instrumentations.
Prior experience using methodologies & tools such as XP, Lean, DevSecOps, SRE, ADO, GitHub, SonarQube, ServiceNow platform capabilities, and agentic AI frameworks.
Ability to travel 10% on average.
Candidates must be located within a commutable distance to the select location.
Ability to work in your local office at a minimum of 3 days per week.
Strong interpersonal skills and professional demeanor.
Ability to manage and prioritize multiple tasks in a fast-paced environment.
Ability to meet deadlines.
Ability to mentor and provide clear guidance to others.

Nice To Haves

ServiceNow Certified System Administrator (CSA)
Certified Application Developer (CAD)
CIS-ITSM
CIS-HRSD
CIS-Data Foundations
AI/GenAI-focused ServiceNow credentials
Cloud/AI certifications such as Azure, AWS, GCP, Kubernetes, or DevOps/SRE credentials.

Responsibilities

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.
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.
Set production standards, lead the design of observability, performance and resilience testing, and operational tooling, and own the admission of systems into production.
Maintain accountability for the operational integrity of production and pre-production environments.
Own SLOs and error budgets; build and operate production observability.
Run performance, ambient-noise, and chaos testing to verify readiness; and guard environments against drift.
Create technical specifications, runbooks, and shared playbooks; lead blameless postmortems; write high-quality, supportable automation; and review the work of other engineers.
Develop lean operational solutions through rapid, inexpensive experimentation to meet the reliability needs of the engineering teams and the business.
Adopt a mindset that favors action and evidence over extensive planning to navigate complexity and uncertainty.
Work collaboratively with empowered, cross-functional partners including engineering, platform engineering, security and risk, data governance, and engineering leadership and architecture.
Co-define service-level objectives with the teams you support, verify readiness, and own the admission decision into production.
Possess deep expertise in site reliability and modern production engineering—cloud platform ownership, observability, performance and capacity engineering, chaos engineering, and cloud/AI cost engineering.
Quickly acquire domain knowledge of the products and platforms you operate and, where they are AI-infused, their distinct production failure modes.
Translate reliability needs, reference architectures, and operational requirements into service-level objectives, runbooks, and production tooling.
Exhibit exceptional communication skills to articulate complex technical concepts clearly and compellingly.
Engage and collaborate with product engineering teams at all organizational levels, including customers as needed.