Machine Learning Engineer – Distributed Cloud

About the role

As a Machine Learning Engineer, you will be responsible for building, deploying, and maintaining scalable ML solutions. You will sit at the intersection of Data Science and Software Engineering, ensuring that models move from research prototypes to reliable production systems.

Key responsibilities

• Design and implement end-to-end ML pipelines for training, testing, and deployment.
• Build and maintain automated pipelines (CI/CD for ML) to ensure seamless model versioning and lifecycle management.
• Fine‑tune model performance and ensure efficient inference at scale.
• Implement observability to track model drift, performance metrics, and system reliability in production.
• Work closely with Data Scientists to translate mathematical models into high‑quality, production‑grade code.

Required profile

• 3+ years of professional experience as an ML Engineer or Backend Engineer with a focus on Machine Learning.
• Strong expertise in Python and solid software engineering principles (OOP, Clean Code).
• Hands‑on experience with TensorFlow, PyTorch, or Scikit‑learn.
• Proficiency in SQL and experience working with large‑scale datasets and data warehousing.
• Familiarity with tools like MLflow, Kubeflow, or DVC for model tracking and orchestration.
• Experience with Cloud platforms (AWS, GCP, or Azure) and containerization using Docker.
• Degree in Computer Science, Mathematics, Engineering, or a related quantitative field.
• Professional level of English (written and spoken).

Required skills

• Python
• TensorFlow
• PyTorch
• Scikit‑learn
• SQL
• MLflow
• Kubeflow
• DVC
• AWS
• GCP
• Azure
• Docker