Machine Learning Engineer GCP Vertex AI Apache Iceberg

Machine Learning Engineer - GCP / Vertex AI / Dataproc / Apache Iceberg Location: Charlotte, NC. No OPT/CPT Key Responsibilities * 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.

Required Skills 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. Ideal Candidate 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!