Machine Learning Engineer GCP Vertex AI Apache Iceberg

IPolarity; Hanover Township (NJ); On-site; USD 140,000 - 190,000; Full time

Job summary

IPolarity is seeking a platform-oriented Machine Learning Engineer to bridge Data Science and Data Engineering on GCP. You will design, deploy, and productionize scalable ML models using Vertex AI, Dataproc, and Iceberg, building end-to-end data lakes and pipelines for batch and real-time scoring.

You will own MLOps, CI/CD, monitoring, and governance, optimizing costs and ensuring reliability across BigQuery, GCS, and related GCP services.

Qualifications

• 7+ years of experience in ML engineering or data engineering.
• Strong experience with GCP and Vertex AI.
• Hands-on Vertex AI, Dataproc, Spark/PySpark/Spark SQL, and Apache Iceberg.
• Proficient in Python and SQL; BigQuery and GCS.
• Experience building distributed data and ML pipelines and MLOps, CI/CD.

Responsibilities

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

Skills

GCP

Vertex AI

Dataproc

Spark / PySpark

Spark SQL

Python

SQL

BigQuery

GCS

MLOps

CI/CD

DevOps

Tools

Docker

Kubernetes

Terraform

Job description

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!