Applied AI Site Reliability Engineer III

About the Role :

We are looking for an experienced Applied AI Site Reliability Engineer III to join a high-performing Product Engineering team responsible for building and operating reliable, scalable, cloud-native platforms and AI-enabled products.

The ideal candidate will combine strong SRE/Production Engineering, Cloud, Observability, Performance Engineering, Kubernetes and Infrastructure-as-Code expertise with hands-on experience operating AI/ML and agentic workloads in production.

Key Responsibilities :

• Own reliability, performance, availability and cost outcomes through SLIs, SLOs, SLAs and error budgets.

• Design and operate production-grade observability across metrics, logs and distributed tracing.

• Build dashboards, actionable alerts, runbooks and automated reliability checks.

• Drive production readiness, release gating and operational acceptance of applications and platforms.

• Manage cloud-native infrastructure across AWS, Azure or GCP.

• Develop and maintain infrastructure using Terraform, Kubernetes, Docker and CI/CD.

• Perform performance, load, capacity and resilience testing.

• Implement chaos engineering and failure-testing practices to improve system resilience.

• Participate in incident management, root-cause analysis and blameless postmortems.

• Identify and eliminate operational toil through automation.

• Support and operate AI/ML and agentic workloads in production.

• Address AI-specific reliability challenges including model drift, train/serve skew, output variance, latency and token/GPU cost anomalies.

• Work with engineering, security, risk, architecture and data teams to establish production standards and controls.

• Contribute to MLOps/LLMOps, AI control planes, model/LLM gateways and guardrails from an operability and performance perspective.

• Drive cloud and AI cost optimization / FinOps initiatives.

• Continuously improve reliability, scalability, security and operational efficiency.

Required Skills & Experience :

• 5+ years of software engineering / SRE / production engineering experience.

• 3+ years working with large-scale, distributed, cloud-native production systems.

• Strong experience with one or more : Python / Go / Bash / Java / C#/.NET, SQL / NoSQL, Kubernetes & Docker, Terraform, CI/CD, GitHub / Azure DevOps.

• Hands-on experience with AWS, Azure or GCP.