Key Responsibilities

• Design and build robust ML pipelines and services for training, validation, and model deployment.
• Collaborate closely with data scientists to facilitate the smooth transition of machine learning models from experimentation phases into production environments.
• Develop and implement reusable infrastructure components, adhering to best practices in DevOps and MLOps.
• Implement high-availability and low-latency model serving infrastructure using REST/gRPC protocols.
• Automate the testing, versioning, and monitoring of ML models through the implementation of CI/CD workflows.

Software Engineering Context

This position falls under the Software Engineering domain, encompassing the entire lifecycle of software solutions. Responsibilities include:

1. Applying scientific methodologies to analyze and resolve complex software engineering challenges.
2. Driving the development and application of software engineering principles and knowledge in research, design, development, and maintenance activities.
3. Exercising original thought and judgment, with the potential to supervise the technical and administrative work of other software engineers.
4. Cultivating skills and expertise within the software engineering discipline to meet professional standards for the applicable role.
5. Fostering collaboration and acting as a team player with fellow software engineers and stakeholders.