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MLOps Senior Researcher in Language Technologies
hace 2 meses
About the Role
The Barcelona Supercomputing Center (BSC) is seeking a highly skilled MLOps Senior Researcher in Language Technologies to join our team. As a leading supercomputing center in Spain, we are committed to research, development, and management of information technologies to facilitate scientific progress.
Key Responsibilities
- Take ownership of the internal product roadmap to ensure continuous delivery of high-quality ML models.
- Design and implement ML model continuous integration, continuous deployment, and continuous monitoring processes for production.
- Develop and maintain CI/CD pipelines for ML models, ensuring seamless deployment and scaling.
- Configure and automate ML model deployment, monitoring, and scaling in production, leveraging state-of-the-art systems and computational infrastructures.
- Implement monitoring and logging of ML models to ensure optimal performance and reliability.
- Collaborate with data engineers and ML engineers to select best ML model architectures and hyperparameters, driving innovation and excellence in our research.
- Ensure the accessibility of open software and the replicability of their deployment via container-based architectures, promoting transparency and reproducibility in our research.
About the Team
The Language Technologies Unit at BSC has a strong track record in several NLP areas, including massive language model building, biomedical text mining, machine translation, and unsupervised learning for under-resourced languages and domains. As an MLOps Senior Researcher in Language Technologies, you will work in a highly sophisticated HPC environment, collaborating with a talented team of researchers and engineers to drive cutting-edge research and innovation.
Requirements
- Experience in designing and shipping internal tools for automating the development, deployment, and monitoring of ML models.
- Strong understanding of ML model continuous integration, continuous deployment, and continuous monitoring processes.
- Proficiency in developing and maintaining CI/CD pipelines for ML models.
- Ability to configure and automate ML model deployment, monitoring, and scaling in production.
- Excellent collaboration and communication skills, with the ability to work effectively with data engineers and ML engineers.
