Applied AI Site Reliability Engineer III
deloitteDallas (TX)
Applied AI Site Reliability Engineer III
Posted today
deloitteDallas (TX)
SENIORITY
Lead
About the role
Overview:
In this role you will ensure production reliability, performance, and cost efficiency for high-visibility products and AI-infused platforms. You will lead reliability practices, observe and optimize systems at scale, and collaborate with cross-functional teams to admit reliable systems into production. Your work combines cloud platform engineering, observability, and AI-operability to deliver resilient, cost-aware solutions. This is a hands-on position with a strong craftsmanship focus and a chance to shape production reliability across the portfolio. Compensation / Benefits discretionary annual incentive program
Responsibilities:
Operate and improve production systems to meet SLOs and manage incident trends within budget Advance observability, performance and resilience testing, and production tooling; gate releases using error budgets and reliability checks Own SLOs, error budgets, incident response, and runbooks; maintain automated, version-controlled dashboards and alerting Lead blameless postmortems and build automation to meet reliability KPIs (availability, performance, cost) Develop lean operational solutions through rapid experimentation and co-define service-level objectives with engineering teams Collaborate with cross-functional partners (engineering, security, data governance) to ensure compliant, reliable delivery Apply AI fluency to operate AI/agentic workloads, implementing SSDLC and end-to-end automation Drive continuous improvement through capacity planning, chaos testing, and cost engineering Translate reliability needs into tooling, runbooks, and production-ready guidance Maintain domain knowledge of products/platforms, including AI failure modes and drift
Key requirements:
5+ years of software engineering and site reliability experience with large-scale cloud-native systems 3+ years in site reliability or production engineering focused on SLIs, SLOs, SLAs, and incident management Proficiency in Python, Go, Bash, Java, C#/.NET, SQL/NoSQL; Kubernetes; Terraform; ArgoCD; CI/CD and observability stacks 3+ years cloud-native engineering with Azure/AWS/GCP, Kubernetes, IaC, networking, multi-environment management Experience operating AI/ML workloads in production, AI/Agentic reliability concepts, MLOps/LLMOps, AI control plane Experience with load/performance testing (LoadRunner, k6, JMeter) and chaos engineering (Azure Chaos Studio, AWS Fault Injector) Knowledge of security controls (RBAC, least-privilege, secrets management) and SDLC governance Familiarity with observability tools (OpenTelemetry, Prometheus, Grafana, Datadog, Dynatrace, CloudWatch, etc.) Ability to travel ~10% and work in selected location with on-site requirements Experience with XP/Lean/DevSecOps, GitHub, MLflow, LangFuse/LangSmith or similar multi-agent orchestration tools Excellent communication Collaborative mindset Strong problem-solving and decision-making Observability design (metrics, tracing, logging) SLO/SLI/availaiblity engineering Chaos engineering and resilience testing
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