AI/Machine Learning Engineer – Distributed Cloud

About the role

We are seeking a versatile and passionate AI / Machine Learning Engineer to join our data science and engineering team. You will bridge the gap between data‑science research and production‑ready applications, building scalable ML systems that drive business value.

Key responsibilities

• Design, develop, and implement end‑to‑end Machine Learning pipelines for training, testing, and deployment of predictive models.
• Work closely with Data Scientists to translate prototypes into scalable, production‑grade code (MLOps).
• Develop robust, efficient, and well‑documented code primarily using Python and ML frameworks such as TensorFlow, PyTorch, and scikit‑learn.
• Implement monitoring and alerting solutions for models in production to track performance, detect drift, and ensure reliability.
• Collaborate with Data Engineers to ensure efficient data preparation, feature engineering, and access to necessary data infrastructure.
• Stay current with the latest advancements in AI, ML techniques, and scalable infrastructure.

Required profile

• 3+ years of professional experience as a Machine Learning Engineer, AI Developer, or similar role.
• Strong expertise in Python and object‑oriented programming with a focus on code quality.
• Practical experience with major ML frameworks (TensorFlow, PyTorch) and libraries (scikit‑learn).
• Solid understanding of MLOps principles and tools for model deployment, versioning, and lifecycle management (e.g., MLflow, Kubeflow).
• Proficiency in SQL and experience working with large datasets and data‑warehousing concepts.
• Experience with cloud platforms (AWS, GCP, or Azure) for model deployment and compute resources.

Required skills

• Python
• TensorFlow
• PyTorch
• scikit‑learn
• MLflow
• Kubeflow
• SQL
• AWS (SageMaker)
• Google Cloud Platform (Vertex AI)
• Microsoft Azure (Azure ML Services)
• Docker
• Kubernetes
• Apache Spark