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# Lead Applied AI Site Reliability Engineer II - PxE A&A

- **Company:** Deloitte T.T.L.
- **Location:** Tampa, FL, United States
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Amazon Web Services, Microsoft Azure, Bash Shell, Cloud Computing, Cloud Engineering, Continuous Integration, Data Structures, Python (Programming Language), Key Management, Load Testing, Object-Oriented Software Development, Pair Programming, Role-Based Access Control, Reliability Engineering, Prometheus, Software Engineering, Datadog, Scripting, Autoscaling, Grafana, Kubernetes, Machine Learning Operations, Cloudwatch, Dynatrace, Golang
- **Published:** August 10, 2026
- **Apply:** https://us.experteer.com/career/view-jobs/lead-applied-ai-site-reliability-engineer-ii-pxe-a-and-a-tampa-fl-usa-58881370

## About the Role

years SLIs/SLOs and ensure readiness * Lead and mentor engineers in reliability practices and continuous improvement * Execute chaos testing, capacity planning, and cost engineering to prove readiness * Maintain hands-on ownership of production and pre-production environments to prevent drift * Communicate technically complex concepts clearly to diverse stakeholders * Foster a collaborative, cross-functional culture focused on quality and trust Tasks * 6+ years of software engineering and SRE experience operating large-scale, cloud-native systems * 3+ years defining/owning SLIs, SLOs, SLAs, and incident management including on-call * 3+ years of cloud-native engineering across Azure/AWS/GCP and Kubernetes, containers, infrastructure-as-code * Experience with CI/CD, observability stacks and production-grade monitoring (Prometheus, Grafana, OpenTelemetry, Datadog, Dynatrace, CloudWatch, etc.) * Experience with AI/ML workloads in production, MLOps, and AI control planes (guardrails, model aa
secrets * Familiar with load testing, chaos engineering, autoscaling, FinOps tooling and GPU/token cost attribution * Strong coding and scripting in languages such as Python, Go, Bash; understanding of OOP/OOD and data structures * Knowledge of security/risk controls including least-privilege, RBAC, secrets management * Excellent communication and leadership abilities; comfortable mentoring and pair programming Key requirements *

## Description

Experteer Overview In this role you drive reliability, performance, and cost efficiency for high-visibility products and AI workloads across cloud environments. You will lead production standards, design observability and resilience tooling, and ensure safe, scalable production releases. You mentor teams, partner with cross-functional groups, and push pragmatic improvements that reduce toil and incidents. This position combines SRE craft with applied AI fluency to operate AI-enabled platforms at scale. You will influence both technology and processes to deliver dependable, cost-aware systems. Compensation / Benefits * Drive reliability and cost outcomes using SLOs and error budgets * Set production standards and design observability, performance, and resilience testing * Own admission of systems into production with readiness checks and automated reliability gates * Develop runbooks, playbooks, and automation; lead blameless postmortems * Collaborate with cross-functional teams to co-define SLIs/SLOs and ensure readiness * Lead and mentor engineers in reliability practices and continuous improvement * Execute chaos testing, capacity planning, and cost engineering to prove readiness * Maintain hands-on ownership of production and pre-production environments to prevent drift * Communicate technically complex concepts clearly to diverse stakeholders * Foster a collaborative, cross-functional culture focused on quality and trust Tasks * 6+ years of software engineering and SRE experience operating large-scale, cloud-native systems * 3+ years defining/owning SLIs, SLOs, SLAs, and incident management including on-call * 3+ years of cloud-native engineering across Azure/AWS/GCP and Kubernetes, containers, infrastructure-as-code * Experience with CI/CD, observability stacks and production-grade monitoring (Prometheus, Grafana, OpenTelemetry, Datadog, Dynatrace, CloudWatch, etc.) * Experience with AI/ML workloads in production, MLOps, and AI control planes (guardrails, model gateways) * Familiar with load testing, chaos engineering, autoscaling, FinOps tooling and GPU/token cost attribution * Strong coding and scripting in languages such as Python, Go, Bash; understanding of OOP/OOD and data structures * Knowledge of security/risk controls including least-privilege, RBAC, secrets management * Excellent communication and leadership abilities; comfortable mentoring and pair programming Key requirements *


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