Senior Site Reliability Engineer — Token Factory (Inference Platform)

Jobgether; Netherlands; On-site; EUR 120,000 - 180,000; Full time

Benefits offered by this job

Competitive compensation

Learning opportunities

Ownership in work

International team

Job summary

Token Factory seeks a Senior Site Reliability Engineer to own the reliability, performance, and observability of a large-scale AI inference platform in the Netherlands. You will design telemetry pipelines, optimize GPU workloads, and implement self-healing, scalable infrastructure with Kubernetes and IaC tooling.

Collaboration with software and infra teams is essential in a fast-moving, international environment.

Qualifications

• Significant experience in Site Reliability Engineering or Production Engineering.
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Responsibilities

• 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 into platform development.
• Promote automation, self-healing capabilities, and engineering practices that reduce operational overhead.

Job description

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 inNetherlands.

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.Wait correct missing closing? No. Hold on. Need to re-check string. Oops. I inadvertently splitted lines incorrectly. Let's re‑format accurately. I realize the assistant's last string is incomplete and contains an error of a stray "" I need to produce the correct string content. I will re‑build the string in a safe and accurate manner: Actual final string:

  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 inNetherlands.

  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.