Job Reference: 331_26_DIR_IBD_DevOps

Closing Date: Saturday, 31 October, 2026

Job title

AI Factory Engineer - DevOps/MLOps

About BSC

The Barcelona Supercomputing Center - Centro Nacional de Supercomputación (BSC-CNS) is the leading supercomputing center in Spain. It houses MareNostrum, one of the most powerful supercomputers in Europe, was a founding and hosting member of the former European HPC infrastructure PRACE (Partnership for Advanced Computing in Europe), and is now hosting entity for EuroHPC JU, the Joint Undertaking that leads large-scale investments and HPC provision in Europe. The mission of BSC is to research, develop and manage information technologies in order to facilitate scientific progress. BSC combines HPC service provision and R&D; into both computer and computational science (life, earth and engineering sciences) under one roof, and currently has over 1000 staff from 60 countries.

Equity, Diversity and Inclusion

We are particularly interested for this role in the strengths and lived experiences of women and underrepresented groups to help us avoid perpetuating biases and oversights in science and IT research. In instances of equal merit, the incorporation of the under‑represented sex will be favoured. We promote Equity, Diversity and Inclusion, fostering an environment where each and every one of us is appreciated for who we are, regardless of our differences. If you consider that you do not meet all the requirements, we encourage you to continue applying for the job offer. We value diversity of experiences and skills, and you could bring unique perspectives to our team.

Context And Mission

The AI Factory is scaling the adoption of AI solutions from experimentation to production.



To support this growth, we are looking for a DevOps/MLOps Engineer who will provide the operational foundations required to deploy, monitor, maintain, and scale AI services reliably and efficiently. The role will be responsible for implementing deployment pipelines, automation, monitoring, environment management, and operational best practices for machine learning and generative AI systems. Working closely with AI engineers, platform teams, infrastructure specialists, and security teams, the successful candidate will help ensure that AI solutions are reproducible, observable, secure, and production‑ready. The mission of this role is to accelerate the operationalization of AI by providing robust MLOps and DevOps capabilities that enable reliable, scalable, and maintainable AI services.

Key Duties

- Design, implement, and maintain CI/CD pipelines for AI and machine learning solutions.
- Support deployment, versioning, release management, and rollback processes for AI services.
- Implement monitoring, logging, alerting, and observability capabilities for AI systems and infrastructure.
- Manage model registries, experiment tracking platforms, and deployment metadata.
- Automate infrastructure provisioning, configuration management, and operational workflows.
- Collaborate with AI engineers, platform teams,



security teams, and cloud administrators.
- Support environment standardization, reproducibility, and operational best practices.
- Define operational procedures, runbooks, and incident response processes.
- Support troubleshooting, performance optimization, and continuous improvement of production AI services.
- Contribute to MLOps, DevOps, and platform engineering standards across the AI Factory.

Requirements

Education

- Bachelor's or Master's degree in Computer Science, Software Engineering, Artificial Intelligence, Telecommunications Engineering, Information Systems, or a related technical discipline.

Essential Knowledge and Professional Experience

- 3–5 years of experience in DevOps, MLOps, Platform Engineering, Site Reliability Engineering (SRE), or related roles.
- Experience designing and operating CI/CD pipelines in production environments.
- Experience with containerization technologies such as Docker and orchestration platforms such as Kubernetes.
- Knowledge of Infrastructure as Code (IaC) practices and automation frameworks.
- Experience with monitoring, logging, observability, and alerting solutions.
- Understanding of machine learning and generative AI deployment lifecycle requirements.
- Experience with model registries, experiment tracking tools, and release management processes.
- Knowledge of security, access control, environment management, and operational governance.
- Strong troubleshooting, automation, and operational problem‑solving skills.

Additional Knowledge and Professional Experience

- Experience supporting LLM, RAG, or Generative AI platforms.
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📌 AI Factory Engineer (Barcelona)
🏢 Barcelona Supercomputing Center
📍 Barcelona