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# Machine Learning Engineer GCP Vertex AI Apache Iceberg

- **Company:** Ipolarity LLC
- **Location:** Hanover, NJ, United States
- **Experience:** Expert
- **Salary:** $93,600.0 - $114,400.0
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Apache HTTP Server, Big Data, BigQuery, Continuous Integration, Information Engineering, Data Governance, DevOps, Distributed Data Store, Monitoring of Systems, Apache Hive, Python (Programming Language), Machine Learning, Metadata, Cloudera, SQL Databases, Management of Software Versions, Data Logging, Feature Engineering, Data Ingestion, Apache Spark, Model Validation, Pyspark, Kubernetes, Data Lineage, Machine Learning Operations, Terraform, Docker
- **Published:** August 24, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=0f4819706c3ee3c8

## About the Role

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!

## Description

* 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.


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