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!