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[QZP635] | Mlops Engineer - Qlm719
Responder al anuncioMLOps 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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