DevOps Engineer
hace 2 días
Zaragoza
We are hiring an MLOps Engineer for a fixed-term contract ending in June 2026, based in Zaragoza or Barcelona (hybrid working model). Por favor, asegúrese de leer completamente el resumen y los requisitos de esta oportunidad de empleo que se detallan a continuación. This opportunity is with a leading European deep-tech company operating at the intersection of AI and advanced computing. The role focuses on deploying and operating large-scale machine learning and large language model systems used by global enterprise clients, including Fortune 500 companies. What is offered: • Competitive annual salary starting from 45,000 EUR, depending on experience and qualifications, • Signing bonus upon joining and a retention bonus at contract completion, • Relocation support if applicable, • Fixed-term contract until June 2026, • Hybrid working model with flexible hours (3 days onsite, 2 days remote), • Design, build, deploy, and monitor ML and LLM pipelines across the full model lifecycle, • Deploy production-grade ML and LLM solutions to enterprise customers, • Implement CI/CD, GitOps, containerization, and orchestration using tools such as Docker and Kubernetes, • Monitor model performance, data drift, and system health, including alerting and truth analysis, • Manage and optimize cloud infrastructure (AWS and/or Azure) for scalability and cost efficiency, • Collaborate closely with Product, DevOps, and AI research teams, • Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field, • 3+ years of experience in MLOps, ML Engineering, LLMOps, or DevOps roles, • Strong experience with public cloud platforms (AWS and/or Azure), • Proficiency xcskxlj in Python and distributed ML frameworks such as Ray, DeepSpeed, FSDP, or Megatron-LM, • Solid understanding of LLM architectures, deployment patterns, and retrieval-based systems, • Experience with CI/CD pipelines, containerized environments, and Kubernetes, • Experience with Mixture-of-Experts models, • Multi-cloud or hybrid cloud environments, • Real-time or streaming ML systems