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.
- Familiarity with MLflow, Kubeflow, Airflow, ArgoCD, or similar MLOps ecosystems.
- Experience in HPC, scientific computing, or large-scale AI infrastructures.
- Experience supporting multi-tenant AI platforms and shared services environments.
- Experience working across infrastructure, AI, security, and platform teams.
- Adaptability in a fast-evolving AI and cloud technology environment.
- Strong DevOps, MLOps, and automation skills.

- Competences

- Ability to build reliable, scalable, and maintainable operational environments.
- Strong analytical and troubleshooting capabilities.
- Proactive, autonomous, and results-oriented mindset.
- Strong focus on reliability, observability, and operational excellence.
- Commitment to continuous improvement and automation.

Conditions

- The position will be located at BSC within the Directors Department
- We offer a full-time contract (35h/week), a good working environment, a highly stimulating environment with state-of-the-art infrastructure, versátil working hours, extensive training plan, restaurant tickets, private health insurance, support to the relocation procedures
- Duration: Open-ended contract due to technical and scientific activities linked to the project and budget duration
- Holidays: 22 days of holidays + 6 personal days + 24th and 31st of December per our collective agreement
- Salary: we offer a competitive salary commensurate with the qualifications and experience of the candidate and according to the cost of living in Barcelona
- Starting date: 16/07/2026

📌 Ai Factory Engineer - Devops/Mlops (Barcelona)
🏢 Barcelona Supercomputing Center
📍 Barcelona