What are the responsibilities and job description for the Machine Learning Engineer GCP Vertex AI Apache Iceberg position at IPolarity LLC?
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!
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!
Salary : $45 - $55