Machine Learning Engineer

• Maintain and further develop our Machine Learning Platform (MLP), ensuring its availability and continuous improvement.
• Develop, deploy, and optimize ML models and algorithms using Google Cloud Platform (GCP) services such as Vertex AI, BigQuery, Cloud Storage, and Pub/Sub.
• Manage and resolve infrastructure-related issues, including failures in GitHub Actions, Docker builds, and disruptions in Vertex AI or BigQuery.
• Support incident management by providing quick and effective interventions in production issues.
• Optimize and restructure codebases to improve maintainability and efficiency.
• Automate and maintain ML pipelines using tools like Kubeflow or Apache Airflow.
• Oversee and manage Kafka topics used by our APIs.
• Provide and maintain VSCode workstations in the Google Cloud environment.
• Assist with deployments, particularly in structural changes such as schema adjustments.
• Manage cross-project repositories, including GitHub projects like geoutils, mlpshared, and WalterRoute.

• Several years of experience with Google Cloud Platform (GCP), especially with Vertex AI, BigQuery, Cloud Storage, Pub/Sub, Cloud Functions, and Cloud Run.
• Strong experience in Kafka.
• Expertise in Infrastructure-as-Code tools like Terraform or Deployment Manager.
• Deep understanding of machine learning methods and model optimization/scaling.
• Proficiency in Python (essential) and familiarity with Java or Go (beneficial).
• Experience with ML frameworks such as TensorFlow, PyTorch, or scikit-learn.
• Knowledge of data modeling, feature engineering, and handling large datasets.
• Hands-on experience with CI/CD pipelines and deployment tools like GitLab CI/CD or Jenkins.
• Familiarity with containerization and orchestration using Docker and Kubernetes.
• Strong analytical and problem-solving skills with a proactive and solution-oriented mindset.
• Excellent communication skills and the ability to work in a team-oriented environment.