> Markdown version of [/jobs/ext/1939488-devops-mlops-engineer-re3](https://www.wearedevelopers.com/jobs/ext/1939488-devops-mlops-engineer-re3). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md).

---

# Devops/Mlops Engineer (Re3)

- **Company:** Barcelona Supercomputing Center
- **Location:** Madrid, Spain
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Airflow, Cloud Computing, Configuration Management, Information Systems, DevOps, Octopus Deploy, Performance Tuning, Release Management, Reliability Engineering, Runbook, Scientific Computating, Software Deployment, Software Engineering, Supercomputing, Management of Software Versions, Data Logging, Large Language Models, Software Troubleshooting, Generative AI, Infrastructure as Code (IaC), Containerization, AI Platforms, Kubernetes, Infrastructure Automation Frameworks, Information Technology, Machine Learning Operations, Docker
- **Published:** August 5, 2026
- **Apply:** https://www.buscojobs.com.es/devops-mlops-engineer-re-3-en-madrid-ID-365501809

## About the Role

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 containerisation 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

## Description

About BSC The Barcelona Supercomputing Center - Centro Nacional de Supercomputación (BSC?CNS) is the leading supercomputing centre in Spain.It houses MareNostrum, one of Europe's most powerful supercomputers, and hosts EuroHPC JU.Its mission is to research, develop, and manage information technologies to facilitate scientific progress.BSC combines HPC service provision and R&D in computer and computational science under one roof and has over **** staff from 60 countries.PositionDevOps/MLOps Engineer (RE3) - Reference 331_26_DIR_IBD_RE3.Full?time, 35 h/week, located at BSC within the Directors Department.Closing date: Friday, 31 July ****.Key DutiesDesign, 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 standardisation, reproducibility, and operational best practices.Define operational procedures, runbooks, and incident?response processes.Support troubleshooting, performance optimisation, and continuous improvement of production AI services.Contribute to MLOps, DevOps, and platform?engineering standards across the AI Factory.RequirementsEducationBachelor'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 Experience3-5 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 containerisation 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 ExperienceExperience 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.CompetencesAbility 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.ConditionsContract: Open?ended full?time (35 h/week) within the Directors Department.Workplace: BSC, flexible working hours, extensive training plan, restaurant tickets, private health insurance, and relocation support.Holidays: 22 days + 6 personal days + the 24th and 31st of December per collective agreement.Competitive salary commensurate with qualifications and the cost of living in Barcelona.Starting date: 16 July ****.Application ProcedureApplicants must submit a CV in English and a cover letter in English via the BSC website.Two references may be required.All documents should be named using the structureName_Surname_CV ,Name_Surname_CoverLetter , etc., and must be under the permitted file sizes.Equal Opportunity StatementBSC?CNS is an equal?opportunity employer committed to diversity and inclusion.We consider all qualified applicants for employment without regard to race, colour, religion, sex, sexual orientation, gender identity, national origin, age, disability or any other basis protected by applicable state or local law.#J-*****-Ljbffr


## Related Videos

- [DevOps for AI: running LLMs in production with Kubernetes and KubeFlow](https://www.wearedevelopers.com/videos/1222-devops-for-ai-running-llms-in-production-with-kubernetes-and-kubeflow)
- [From Messy Queries to Scalable Systems - How Data Engineering actually works](https://www.wearedevelopers.com/videos/100203-from-messy-queries-to-scalable-systems-how-data-engineering-actually-works)
- [From DevOps to Scaled DevOps: How We’re Rebuilding Continuous Delivery as a Platform](https://www.wearedevelopers.com/videos/100018-from-devops-to-scaled-devops-how-we-re-rebuilding-continuous-delivery-as-a-platform)
- [Docker Compose: Rediscovered](https://www.wearedevelopers.com/videos/1978-docker-compose-rediscovered)
- [AI Model Management Life Circles: ML Ops For Generative AI Models From Research to Deployment](https://www.wearedevelopers.com/videos/1152-ai-model-management-life-circles-ml-ops-for-generative-ai-models-from-research-to-deployment)
- [Effective Machine Learning - Managing Complexity with MLOps](https://www.wearedevelopers.com/videos/185-effective-machine-learning-managing-complexity-with-mlops)

## Related Articles

- [MLops – Deploying, Maintaining And Evolving Machine Learning Models in Production](https://www.wearedevelopers.com/magazine/115-mlops-deploying-maintaining-and-evolving-machine-learning-models-in-production)
- [MLOps – What’s the deal behind it?](https://www.wearedevelopers.com/magazine/125-mlops-what-s-the-deal-behind-it)
- [MLOps And AI Driven Development](https://www.wearedevelopers.com/magazine/82-mlops-and-ai-driven-development)
- [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer)
- [What Are Large Language Models?](https://www.wearedevelopers.com/magazine/304-what-are-large-language-models)
- [Is Software Engineering Over-Saturated?](https://www.wearedevelopers.com/magazine/418-is-software-engineering-over-saturated)