Applied AI Site Reliability Engineer III - PxE Talent

About the role

The Applied AI Site Reliability Engineer III plays a critical role in ensuring the reliability, performance, and operational integrity of high-visibility products and platforms. This position requires a hands-on approach to managing cloud platform engineering, observability, and performance and reliability engineering, with a focus on applying AI to ensure the smooth operation of AI and agentic workloads.

Responsibilities

• Drive accountability for reliability, performance, and cost outcomes, measured in service-level objectives and error budgets, not raw uptime.
• Operate products, platforms, and environments to meet their Service Level Objectives (SLOs) within budget, and track incident trends and toil to prioritize work that most improves reliability.
• Maintain accountability for the operational integrity of production and pre-production environments, and for the production standards that systems are admitted against.
• Own Service Level Indicators (SLIs), Service Level Objectives (SLOs), and Service Level Agreements (SLAs); manage error budgets; conduct incident command and on-call rotations; build and operate production observability (metrics, tracing, logging); prevent environment drift; and enforce segregation-of-duty controls in partnership with security and risk.
• Adopt a mindset that favors action and evidence over extensive planning, adopting a leaning-forward approach to reliability through incremental, measurable improvements.
• Develop lean operational solutions through rapid, inexpensive experimentation to meet the reliability needs of the engineering teams and the business.
• Work collaboratively with cross-functional partners to uphold production standards and integrate their constraints, co-defining service-level objectives and operational readiness.
• Translate reliability needs, reference architectures, and operational requirements into service-level objectives, runbooks, and production tooling.
• Operate as an infrastructure-focused engineer, not a tool operator, creating technical specifications, runbooks, and shared playbooks; lead blameless postmortems that turn incidents into learning and systemic fixes; and write high-quality, supportable automation to ensure all reliability KPIs (availability, performance, and cost) are met or exceeded.

Qualifications

• Bachelor's degree in computer science, software engineering, data science, machine learning, or related discipline.
• 5+ years of software engineering and site reliability engineering experience operating large-scale, distributed, cloud-native systems in production.
• Experience in defining and owning SLIs, SLOs, and SLAs; error budgets; incident command and on-call; building and operating production observability; environment integrity and drift prevention; and segregation-of-duties controls 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.
• Prior experience operating AI/ML and agentic workloads in production, including their reliability failure modes, MLOps/LLMOps, and the AI control plane.
• Experience with load and performance testing under simulated production traffic, chaos engineering, capacity planning, autoscaling, and cloud/AI cost engineering.
• 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.
• 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).
• Ability to work in your local office at a minimum of 3 days per week.

Skills

• Python, Go, Bash, Java, C#, .NET, SQL/NoSQL, Kubernetes, Terraform, ArgoCD, CI/CD, observability stacks.
• Cloud-native engineering and cloud platform ownership on Azure, AWS, or GCP.
• Experience with Azure OpenAI, AWS Bedrock, or Vertex AI, container orchestration, infrastructure-as-code, networking, and multi-environment management.
• Experience with load and performance testing, chaos engineering, capacity planning, autoscaling, and cloud/AI cost engineering.
• Experience with Business Context Diagrams, sequence/activity/state/entity relationship/data flow diagrams, OOP/OOD, data structures, algorithms, and code instrumentations, and AI-augmented spec-driven development.
• Experience with methodologies & tools such as XP, Lean, DevSecOps, SRE, ADO, GitHub, SonarQube, MLflow, and agentic AI frameworks.

Benefits

Deloitte offers a comprehensive benefits package including health insurance, retirement plans, and paid time off.

Pay

$102,500 to $210,600 annually.

Schedule

Minimum of 3 days per week in the office, with occasional travel required.