MLOps Engineer
Office based in Barcelona.
This is what you will do:
The MLOps Engineer reports to the IT Director of Insights and Analytics and is a critical role in Alexion IT RDU organization. You will be a key member of our IT team and play a crucial role in developing and implementing innovative machine learning solutions for our business. Your expertise in MLOps will be critical in designing, building, and deploying production-ready machine learning models at scale.
You will be responsible for:
Leading the development and implementation of MLOps infrastructure and tools for machine learning models.Collaborating with cross-functional teams to identify, prioritize and solve business problems using machine learning techniques.Designing, developing and implementing production-grade machine learning models that meet business requirements.Overseeing the training, testing, and validation of machine learning models.Ensuring that machine learning models meet high-quality standards, including scalability, maintainability, and performance.Designing and implementing efficient development environments and processes for ML applications.Communicating with stakeholders and senior management to provide updates on the progress of machine learning projects.Developing assets, accelerators and thought capital for your practice by providing best in class framework and reusable components.Developing and maintaining MLOps pipelines to automate machine learning workflows and integrating them with existing IT systems.Integrating Generative AI models based solutions within the broader machine learning ecosystem, ensuring they adhere to ethical guidelines and serve the intended business purposes.Implementing robust monitoring and governance mechanisms for Generative AI models based solutions to ensure they evolve in alignment with business needs and regulatory standards.You will need to have:
Bachelor's degree in Computer Science, Electrical Engineering, Mathematics, Statistics, or a related field.4+ years of experience in developing and deploying machine learning models in production environments.Hands-on experience building production models with focus on data science operations including serverless architectures, Kubernetes, Docker/containerization, and model upkeep and maintenance.Familiarity with API-based application architecture and API frameworks.Experience with CICD orchestration frameworks, such as GitHub Actions, Jenkins or Bitbucket pipelines.Deep understanding of software development lifecycle and maintenance.Extensive experience with one or more orchestration tools (e.g. Airflow, Flyte, Kubeflow).Experience working with MLOps tools like experiment tracking, model registry tools and feature stores.
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