Applied AI Site Reliability Engineer I (PxE A&A)

Deloitte • Tampa, FL

8d • $69,400 - $115,700

About The Position

The Applied AI Site Reliability Engineer I is a hands-on engineering role focused on the reliability, performance, and operational integrity of high-visibility products, platforms, and their environments. The engineer helps keep production safe, performant, and cost-effective, applying cloud platform engineering, observability, performance and reliability engineering, and applied AI expertise to operate AI and agentic workloads alongside other systems. The role collaborates across teams to uphold production standards, safeguard environments, and support confident production admission.

The engineer owns and improves service-level objectives (SLOs) and error budgets; builds production observability and actionable alerting; conducts readiness, performance, capacity, and resilience testing; prevents environment drift; and develops automation, runbooks, specifications, and shared playbooks. The role also supports incident response and blameless postmortems, works with product and engineering teams to define operational readiness, and advances reliability through measurable, iterative improvements.

The successful candidate will work with engineering, platform engineering, security and risk, data governance, architecture, and leadership partners. They will learn the products and their operational failure modes, including AI-specific issues such as drift, train/serve skew, latency, output variance, and token or GPU cost anomalies. The role emphasizes maintainable engineering, effective communication, collaborative problem-solving, and continuous learning.

US Deloitte Technology Product Engineering delivers internal and go-to-market digital solutions for Deloitte businesses, service lines, and operations. The position is based in Tampa, Florida. Travel is expected to average 10%, based on the work and products built. The disclosed estimated wage range is $69,400 to $115,700, with compensation decisions dependent on relevant factors. A discretionary annual incentive may also be available, subject to program rules and applicable factors.

Requirements

• Bachelor’s degree in computer science, software engineering, data science, machine learning, or a related discipline; experience is stated to be the most relevant factor.
• At least 2 years of software engineering and site reliability engineering experience operating large-scale, distributed, cloud-native systems in production.
• Experience with some of the following: Python, Go, Bash, Java, C#/.NET, SQL/NoSQL, Kubernetes, Terraform, ArgoCD, CI/CD, and observability stacks.
• At least 1 year of site reliability or production engineering experience for large-scale systems, including defining and owning SLIs, SLOs, SLAs, and error budgets; incident command and on-call; production observability; environment integrity and drift prevention; and segregation-of-duties controls in partnership with security and risk.
• At least 1 year of cloud-native engineering and cloud platform ownership on Azure, AWS, or GCP, including cloud AI/ML services, container orchestration, infrastructure as code, networking, and multi-environment management.
• Prior experience operating AI/ML and agentic workloads in production, including reliability failure modes, MLOps/LLMOps, and AI control-plane operations from an operability and performance perspective.
• Prior experience with load and performance testing under simulated production traffic, chaos engineering, capacity planning, autoscaling, and cloud/AI cost engineering, including GPU/inference and token cost attribution.
• Prior software engineering experience and understanding of Business Context Diagrams, sequence/activity/state/entity relationship/data flow diagrams, OOP/OOD, data structures, algorithms, code instrumentation, and AI-augmented spec-driven development.
• Experience with methodologies and tools such as XP, Lean, DevSecOps, SRE, ADO, GitHub, SonarQube, MLflow, and agentic AI frameworks such as LangFuse, LangSmith, or equivalent multi-agent orchestration tools.
• Ability to travel an average of 10%, based on the work and products built.
• Excellent interpersonal and organizational skills and the ability to handle diverse situations, complex projects, and changing priorities with passion, empathy, and care.
• Limited immigration sponsorship may be available.

Responsibilities

• Operate supported products, platforms, and environments to meet SLOs and cost targets; monitor incident trends and toil to prioritize reliability improvements.
• Advocate for production reliability and operability, uphold production standards, and support system admission and release decisions using error budgets and automated reliability checks.
• Own SLOs and error budgets and maintain production and pre-production environment integrity.
• Build and operate version-controlled observability, including dashboards and actionable, SLO-driven alerting, and feed operational insights back into engineering.
• Run performance, ambient-noise, chaos, and readiness testing; verify system readiness and guard environments against drift.
• Create technical specifications, runbooks, shared playbooks, and high-quality automation to meet availability, performance, and cost KPIs.
• Lead blameless postmortems that convert incidents into learning and systemic fixes.
• Work with engineering and business teams before, during, and after delivery to define SLOs and operational readiness without unnecessarily blocking delivery.
• Improve reliability incrementally through measurable resilience testing and SLO refinement.
• Collaborate with engineering, platform engineering, security and risk, data governance, architecture, and engineering leadership; integrate their constraints into production practices.
• Operate AI and agentic workloads reliably, including applied AI and agentic SSDLC workflows and automated quality checks across the software development lifecycle.
• Acquire domain knowledge and translate reliability needs, reference architectures, and operational requirements into SLOs, runbooks, and production tooling.
• Communicate technical concepts and evidence-based trade-offs clearly, build constructive relationships, and align teams on feasible solutions.
• Travel an average of 10%, based on the work and products built.

Benefits

• A discretionary annual incentive program may be available, subject to program rules and relevant performance and organizational factors.
• A broad range of employee benefits is offered; specific benefits are not listed in the posting.
• Professional development opportunities, including skill-building, leadership opportunities, mentorship, on-the-job learning, and formal development programs.

What This Job Offers

• Job Type: Full-time
• Career Level: Entry Level