Senior Site Reliability Engineer — Token Factory (Inference Platform)

Accountabilities

• Own the reliability, performance, and observability of the inference platform and its supporting infrastructure.


• Design, implement, and continuously improve telemetry pipelines covering metrics, logs, and traces.


• Build monitoring and observability solutions capable of processing large volumes of production signals and converting them into actionable insights.


• Configure and optimize Kubernetes infrastructure for high availability, scalability, and efficient resource utilization.


• Tune Kubernetes autoscaling mechanisms to improve the efficiency and utilization of GPU resources.


• Develop and maintain Terraform modules and infrastructure-as-code patterns that embed resilience and reliability into new clusters and services.


• Design and improve request-routing, retry, and failure-handling mechanisms to minimize the impact of transient infrastructure or service failures.


• Develop automation and operational tooling to detect, isolate, and remediate incidents quickly.


• Create, maintain, and improve runbooks for incident response and operational procedures.


• Participate in production incident management, troubleshooting issues and restoring services within demanding reliability objectives.


• Lead or contribute to post-mortem processes and implement corrective actions to prevent recurring incidents.


• Define and improve reliability practices for high-throughput APIs, including alerting strategies and Service Level Objectives (SLOs).


• Investigate distributed-system failures and performance issues across infrastructure and application layers.


• Optimize systems from the kernel and infrastructure layer through to the application layer.


• Support and improve the operation of GPU-intensive inference workloads and accelerator-based infrastructure.


• Contribute to scaling the inference platform while balancing performance, reliability, and infrastructure costs.


• Collaborate closely with software engineers to incorporate reliability and operational excellence into product and platform development.


• Promote automation, self-healing capabilities, and engineering practices that reduce operational overhead and improve system resilience.

Requirements

• Significant experience in Site Reliability Engineering, Production Engineering, DevOps, or a closely related infrastructure discipline.


• Deep practical knowledge of Kubernetes in production environments.


• Strong experience with Prometheus and Grafana for monitoring, metrics, dashboards, and observability.


• Advanced experience with Terraform and infrastructure-as-code practices.


• Strong scripting and automation skills using Python and/or Bash.


• Solid understanding of distributed systems and the ways production backends can fail under real-world conditions.


• Experience designing effective alerts, monitoring strategies, and SLOs for high-throughput services or APIs.


• Strong troubleshooting and debugging skills across infrastructure, networking, operating systems, and application layers.


• Experience designing systems for high availability, resilience, scalability, and graceful failure recovery.


• Hands-on experience with GPU-heavy workloads or accelerator-based infrastructure is highly valuable.


• Familiarity with GPU inference technologies such as vLLM, Triton, Ray, or comparable accelerator and model-serving stacks.


• Experience with MLOps, model hosting, AI infrastructure, or machine-learning platforms is advantageous.


• Strong understanding of infrastructure automation, deployment, configuration management, and operational tooling.


• Ability to analyze complex performance and reliability problems and translate findings into practical engineering improvements.


• Strong incident-management and root-cause-analysis capabilities.


• Ability to collaborate effectively with software engineers and other technical teams to integrate reliability into platform development.


• Proactive mindset with a strong focus on automation, self-healing systems, and continuous improvement.


• Comfortable working independently, taking ownership of critical infrastructure, and operating effectively in a fast-paced technical environment.

• Competitive compensation.


• Career growth and continuous learning opportunities.


• Flexibility and significant ownership in your work.


• Collaborative and innovative international working environment.


• Opportunity to work on high-impact AI infrastructure and inference technologies.


• Exposure to large-scale GPU infrastructure and complex distributed systems.


• Opportunity to contribute to infrastructure supporting next-generation multimodal AI applications.


• Diverse and highly technical international teams.


• Inclusive workplace committed to equal employment opportunities.


• Workplace accommodations available throughout the application process where required.


• Employment is subject to authorization to work in the country where the position is based.

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