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 deployment and monitoring.
Innovative and focused on AI 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 involve setting up version control systems, continuous integration and deployment (CI/CD) pipelines, and monitoring models for performance and drift. You'll also be tasked with ensuring that models are retrained responsibly to maintain their effectiveness over time.
The ideal candidate for this position should have a solid understanding of MLOps, including familiarity with tools like Git for version control and cloud deployment patterns. You will work closely with data scientists and other engineers to ensure that the machine learning lifecycle is well-managed and optimized for performance.
This role suits individuals who are detail-oriented and have a strong background in machine learning and software engineering. If you enjoy working in a collaborative environment and are eager to contribute to innovative AI solutions, this position could be a great fit for you.
Key requirements include: • Experience with MLOps practices • Proficiency in version control systems, particularly Git • Knowledge of CI/CD processes • Familiarity with cloud deployment strategies
If you are ready to take on the challenge of improving machine learning workflows, we encourage you to apply.
You'll be taken to the original listing on PNet to apply.