Machine Learning Engineer GCP Vertex AI Apache Iceberg

• Deploy and manage ML models using Google Vertex AI.
• Build automated ML pipelines for batch and near real-time scoring.
• Develop scalable data processing pipelines using Dataproc, Apache Spark, PySpark, and Spark SQL.
• Design and optimize large-scale data lakes using Apache Iceberg.
• Implement partitioning, schema evolution, versioning, and time-travel capabilities.
• Build data ingestion, transformation, and feature engineering workflows.
• Implement MLOps, CI/CD, model monitoring, retraining, and automation.
• Work with BigQuery and Google Cloud Storage (GCS).
• Monitor model performance, pipeline health, logging, metrics, and alerts.
• Optimize GCP compute resources and cloud costs.
• Support production incidents, reliability, security, and governance.

7+ years of experience in Machine Learning Engineering, Data Engineering, or related areas. Strong GCP experience. Hands-on Vertex AI experience. Dataproc. Apache Spark / PySpark / Spark SQL. Apache Iceberg. Python and SQL. BigQuery and GCS. Experience building distributed data and ML pipelines. Strong understanding of MLOps and ML model lifecycle management. CI/CD and DevOps experience.

Preferred Skills.

• Vertex AI Pipelines / Kubeflow Pipelines.
• Docker / Kubernetes.
• Feature Stores.
• Model Monitoring.
• Terraform / Infrastructure as Code.
• Data Governance / Metadata / Data Lineage.
• Financial Services, AML, Fraud, Risk Analytics, or regulated environments., We are looking for a platform-oriented Machine Learning Engineer who can bridge the gap between Data Science and Data Engineering and transform ML models into scalable, governed, production-ready solutions on GCP. If you have strong experience with GCP + Vertex AI + Dataproc/PySpark + Apache Iceberg + MLOps, we’d love to connect!