As an AI Engineer, you'll focus on implementing MLOps practices to streamline machine learning workflows. This role is ideal for those who are passionate about optimizing model performance and deployment.
Innovative and focused on technology-driven solutions.
In this role, you'll be responsible for implementing MLOps practices that enhance the efficiency and reliability of machine learning models. Your day-to-day tasks will include setting up version control systems, continuous integration and delivery (CI/CD) pipelines, and monitoring model performance to detect any drift. You'll also be involved in responsible retraining of models to ensure they remain effective over time.
The ideal candidate will have a strong familiarity with MLOps, version control tools like Git, and cloud deployment patterns. You'll work closely with data scientists and other engineers to ensure that models are not only built but also deployed and maintained effectively in production environments.
This position suits individuals who are detail-oriented and have a strong technical background in machine learning and software engineering. If you are eager to contribute to innovative projects and improve machine learning workflows, this could be the right fit for you.
Key requirements include a solid understanding of MLOps practices, experience with version control systems, and knowledge of CI/CD processes. Familiarity with cloud platforms will also be beneficial as you work on deploying models in various environments.
You'll be taken to the original listing on PNet to apply.