Senior Site Reliability Engineer — Token Factory (Inference Platform)

Jobgether; Lavamünd; Vor Ort; EUR 127.000 - 190.000; Vollzeit

Benefits dieser Stelle

Competitive compensation

Career growth

Learning opportunities

Ownership in work

International teams

Zusammenfassung

Token Factory is seeking a Senior Site Reliability Engineer to own reliability, performance, and observability for a large-scale AI inference platform. You will design telemetry pipelines, monitor production signals, and optimize GPU-heavy workloads with Kubernetes and IaC tooling.

You will collaborate with software and infra teams to build self-healing systems, automate incidents, and improve runbooks and post-mortems to prevent incidents.

Qualifikationen

• Significant experience in Site Reliability Engineering or Production Engineering.
• Deep practical knowledge of Kubernetes in production environments.
• Strong experience with Prometheus and Grafana for monitoring and observability.
• Advanced experience with Terraform and infrastructure-as-code practices.
• Strong scripting and automation skills using Python and Bash.
• Experience designing alerts, SLOs, and monitoring strategies for high-throughput services.
• Hands-on experience with GPU-heavy workloads or accelerator-based infra is valuable.

Aufgaben

• Own reliability, performance, and observability of the inference platform.
• Design telemetry pipelines covering metrics, logs, and traces.
• Build monitoring solutions for large production signal volumes.
• Configure Kubernetes for high availability, scalability, and resource efficiency.
• Tune autoscaling to optimize GPU resources.
• Develop and maintain Terraform modules and IaC patterns.
• Improve request-routing, retries, and failure handling.
• Develop automation to detect, isolate, and remediate incidents.
• Create runbooks for incident response and operations.
• Lead post-mortems and implement corrective actions.

Kenntnisse

SRE

Kubernetes

Prometheus

Grafana

Terraform

Python

Bash

Incident management

GPU workloads

MLOps

Tools

Kubernetes tooling

Terraform modules

Jobbeschreibung

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Senior Site Reliability Engineer — Token Factory (Inference Platform) based in Switzerland.

This is a senior engineering role focused on the reliability, performance, and observability of a large-scale AI inference platform.
You will help operate infrastructure serving foundation models across text, vision, audio, and emerging multimodal workloads.
The role combines Kubernetes, infrastructure-as-code, observability, automation, and production incident management at significant scale.
You will optimize GPU-heavy workloads, strengthen resilience, and ensure high-throughput APIs meet demanding reliability and cost targets.
You will work closely with software engineers and infrastructure teams to build self-healing systems and robust operational processes.
The environment is fast-moving, highly technical, international, and focused on solving complex infrastructure challenges for the AI ecosystem.
This is an opportunity to have a direct impact on the infrastructure powering next-generation AI applications.

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.

Benefits:

• 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.