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

Deloitte • Tampa, FL

23h • $69,400 - $127,900 • Onsite

About The Position

The Applied AI Site Reliability Engineer I takes a hands-on role in the reliability, performance, and operational integrity of high-visibility products, platforms, and their environments. The engineer helps keep production safe, performant, and cost-effective by 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 with cross-functional teams to uphold production standards, safeguard environments, and support confident admission of systems into production.

The position focuses on measurable reliability, performance, and cost outcomes, using service-level objectives and error budgets. It owns operational readiness and environment integrity, builds production observability and automation, tests performance and resilience, and helps teams improve systems through incident learning and incremental changes. The engineer partners with product and platform teams, security and risk, data governance, and engineering leadership to define operational requirements and deliver supportable, compliant solutions without becoming a delivery bottleneck.

The role is part of US Deloitte Technology Product Engineering, Deloitte’s internal development team, which builds and deploys digital solutions for Deloitte’s businesses, service lines, and internal operations. The position is available in Dallas, Texas, or Tampa, Florida, and may involve travel averaging 10%.

Requirements

• Bachelor’s degree in computer science, software engineering, data science, machine learning, or a related discipline; relevant experience is the most important factor.
• At least 2 years of software engineering and site reliability engineering experience operating large-scale, distributed, cloud-native production systems.
• Experience with some of 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 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 such as drift, train/serve skew, and output variance; MLOps/LLMOps; and AI control-plane operability and performance.
• 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 using 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 supported.
• Excellent interpersonal and organizational skills, including the ability to handle diverse situations, complex projects, and changing priorities with passion, empathy, and care.
• Exceptional communication skills, including the ability to explain complex technical concepts and influence teams using well-structured, evidence-supported arguments and trade-offs.

Responsibilities

• Own reliability, performance, and cost outcomes for supported products, platforms, and environments, using service-level objectives and error budgets.
• Track incident trends and operational toil to prioritize improvements that increase reliability and maintain lean, safe production operations.
• Advocate for production reliability and operability; uphold production standards and contribute to observability, performance, resilience testing, and operational tooling designs.
• Support system admission into production, including release gating based on error budgets and automated reliability checks, readiness verification, environment integrity, and operational support.
• Define and own SLOs and error budgets; build and operate version-controlled dashboards and actionable, SLO-driven alerts, and feed operational findings back to engineering teams.
• Run performance, ambient-noise, and chaos testing; verify readiness and prevent environment drift across production and pre-production environments.
• Create technical specifications, runbooks, shared playbooks, and high-quality automation to meet availability, performance, and cost reliability KPIs.
• Lead blameless postmortems and turn incidents into learning and systemic fixes.
• Work with engineering teams before, during, and after delivery to co-define SLOs and operational readiness, developing lean solutions without becoming a delivery bottleneck.
• Improve reliability incrementally through measurable changes, progressive resilience testing, and SLO refinement.
• Collaborate with engineering, platform engineering, security and risk, data governance, and engineering leadership and architecture; integrate their constraints and maintain segregation-of-duties controls.
• Operate AI and agentic workloads reliably, including their production lifecycle, quality checks, and distinct operational failure modes.
• Acquire domain knowledge of operated products and platforms, translating reliability needs, reference architectures, and operational requirements into SLOs, runbooks, and production tooling.
• Communicate technical concepts, evidence-based trade-offs, and business-aligned solutions; build constructive relationships and consensus with product engineering teams and customers as needed.
• Continuously learn and apply modern production engineering practices across cloud platforms, observability, performance and capacity engineering, chaos engineering, and cloud/AI cost engineering.

Benefits

• Employees are offered a broad range of benefits.
• Opportunities to build new skills, take on leadership opportunities, and grow through mentorship and formal development programs.
• May be eligible to participate in a discretionary annual incentive program, subject to program rules and applicable factors.

What This Job Offers

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