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 Spain.

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.

Data Privacy Notice:

By submitting your application, you acknowledge that Jobgether will process your personal data to evaluate your candidacy and share relevant information with the hiring employer. This processing is based on legitimate interest and pre-contractual measures under applicable data protection laws (including GDPR). You may exercise your rights (access, rectification, erasure, objection) at any time.

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📌 Senior Site Reliability Engineer — Token Factory (Inference Platform) (Madrid)
🏢 Jobgether
📍 Madrid