Lead Applied AI Site Reliability Engineer II - PxE ERM

Lead Applied AI Site Reliability Engineer II

Role Overview: As a 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.

Key Responsibilities:

• 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. Set 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; and review the work of other engineers, mentoring them, 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 and Integration: Work collaboratively with empowered, cross-functional partners: engineering, platform engineering, security and risk, data governance, and engineering leadership and architecture. Set 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 own 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.
• Advanced Technical Proficiency: Possess deep 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. Be a role model, leveraging these techniques to optimize reliability, performance, and operational delivery. Demonstrate strong understanding of the full lifecycle of platform and product development, focusing on continuous improvement and learning.
• Domain Expertise: 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.
• Effective Communication and Influence: Exhibit exceptional communication skills, capable of articulating complex technical concepts clearly and compellingly. Inspire and influence teammates and product teams through well-structured arguments and trade-offs supported by evidence. Create coherent narratives that align technical solutions with business objectives.
• Engagement and Collaborative Co-Creation: Engage and collaborate with product engineering teams at all organizational levels, including customers as needed. Build and maintain constructive relationships, fostering a culture of co-creation and shared momentum towards achieving product goals. Align diverse perspectives and drive consensus to create feasible solutions.

The team: US Deloitte Technology Product Engineering has modernized software and product delivery, creating a scalable, cost-effective model that focuses on value/outcomes that leverages a progressive and responsive talent structure. As Deloitte’s primary internal development team, Product Engineering delivers innovative digital solutions to businesses, service lines, and internal operations with proven bottom-line results and outcomes. It helps power Deloitte’s success. It is the engine that drives Deloitte, serving many of the world’s largest, most respected companies. We develop and deploy cutting-edge internal and go-to-market solutions that help Deloitte operate effectively and lead in the market. Our reputation is built on a tradition of delivering with excellence.

The successful candidate will possess:

• Excellent interpersonal and organizational skills, with the ability to handle diverse situations, complex projects, and changing priorities, behaving with passion, empathy, and care.

Required Qualifications:

• A bachelor’s degree in computer science, software engineering, data science, machine learning, or related discipline. Experience is the most relevant factor.
• 6+ years of software engineering and site reliability engineering experience operating large-scale, distributed, cloud-native systems in production, with experience 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—defining and owning SLIs, SLOs, and SLAs; error budgets; incident command and on-call; building and operating production observability (metrics, tracing, logging—e.g., OpenTelemetry, Prometheus, Grafana, Datadog, Dynatrace, Amazon CloudWatch, Azure Monitor, Google Cloud Operations, SolarWinds, Splunk); environment integrity and drift prevention across pre-production and production; and segregation-of-duties controls (least-privilege/RBAC, deploy approvals, secrets management) in partnership with security and risk.
• 3+ years of experience with cloud-native engineering and cloud platform ownership on any of the cloud hyperscalers such as Azure, AWS, or GCP—including their AI/ML services such as Azure OpenAI, AWS Bedrock, or Vertex AI—plus container orchestration (Kubernetes, Docker), infrastructure-as-code, networking, and multi-environment management.
• 1+ years of experience establishing reliability and operational standards—SLO discipline, runbooks, and performance and resilience budgets—including actively leading, mentoring, and guiding team members in the adoption and continuous improvement of these standards.
• Prior experience operating AI/ML and agentic workloads in production—their reliability failure modes (drift, train/serve skew, output variance), MLOps/LLMOps, and the AI control plane (model/LLM gateway, guardrails) from the operability and performance side.
• Prior experience with load and performance testing under simulated production traffic (e.g., LoadRunner, k6, or JMeter), chaos engineering (e.g., Azure Chaos Studio, AWS Fault Injector), capacity planning, autoscaling, and cloud/AI cost engineering (FinOps tooling/dashboards, including GPU/inference and token cost attribution).
• Prior software engineering experience with the understanding of Business Context Diagrams (BCD), sequence/activity/state/entity relationship/data flow diagrams, OOP/OOD, data structures, algorithms, and code instrumentations, and AI-augmented spec-driven development.
• Prior experience using methodologies & tools such as XP, Lean, DevSecOps, SRE, ADO, GitHub, SonarQube, MLflow, and agentic AI frameworks (e.g. LangFuse, LangSmith, or equivalent multi-agent orchestration tools) etc. to operate high-quality, resilient platforms and products at scale.
• Candidates must be located within a commutable distance to one of the select locations available for this role 
• Ability to work in your local office at a minimum of 3 days per week

Other:

• Ability to travel 10%, on average, based on the work you do and products you build.
• Limited immigration sponsorship may be available.

The wage range for this role takes into account the wide range of factors that are considered in making compensation decisions including but not limited to skill sets; experience and training; licensure and certifications; and other business and organizational needs. The disclosed range estimate has not been adjusted for the applicable geographic differential associated with the location at which the position may be filled. At Deloitte, it is not typical for an individual to be hired at or near the top of the range for their role and compensation decisions are dependent on the facts and circumstances of each case. A reasonable estimate of the current range is $113100 to $232300.

You may also be eligible to participate in a discretionary annual incentive program, subject to the rules governing the program, whereby an award, if any, depends on various factors, including, without limitation, individual and organizational performance.

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