Role overview
We are seeking a passionate and skilled Machine Learning Engineer to join our forward-thinking team.
As a Machine Learning Engineer, you will be responsible for designing, developing, and deploying machine learning models that drive key business decisions and enhance our product offerings. You will work closely with data scientists, data engineers, and software developers to integrate machine learning solutions into our existing systems.
What you'll work on
- Model Development: Collaborate with data scientists to design and implement models that address business and product needs.
- Model Deployment: Develop and maintain infrastructure for deploying models into production, ensuring scalability and reliability.
- Data Processing: Work with data engineers to ensure data is clean, well-structured, and accessible for model training and evaluation.
- Performance Optimization: Continuously monitor and optimize model performance, implementing improvements as necessary.
- Collaboration: Work closely with cross-functional teams to integrate machine learning solutions into products and services.
- Innovation: Stay up-to-date with the latest advancements in machine learning and apply them to improve our solutions.
- Evolution: Learn about our ML Platform and help the team to keep improving and growing.
What we're looking for
- Bachelor's or Master's degree in Computer Science, Engineering, or a related field.
- 2+ years of experience in machine learning or a related field, not focused in the science layer, more on the technical one.
- Proficiency in programming languages such as Python.
- Experience with machine learning frameworks and libraries (e.g., MLflow mainly if possible, but can be TensorFlow, PyTorch).
- Familiarity with cloud platforms (e.g., AWS, GCP) and containerisation technologies (e.g., Docker, Kubernetes).
- Strong problem-solving skills and the ability to work collaboratively in a team environment.
- Excellent communication skills, with the ability to explain complex technical concepts to non-technical stakeholders.
Tags & focus areas
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