GCP ML Platform Engineer - Vertex AI & Iceberg

TechDigital Group; Charlotte (NC); On-site; USD 120,000 - 190,000; Full time

Job summary

TechDigital Group is seeking a seasoned Machine Learning Engineer to build, deploy, and manage scalable ML solutions on Google Cloud Platform. You will operationalize models developed by data scientists, ensure reliable execution, monitoring, and integration with enterprise data platforms using Vertex AI, Dataproc, Apache Spark, and Iceberg.

You will design end-to-end ML pipelines, manage training and endpoints, implement CI/CD for ML, and collaborate with data engineers, architects, and DevOps

Qualifications

• Bachelor's degree in CS/Engineering/Data Science or related field.
• 5+ years of experience in Data Engineering or ML Engineering roles.
• Strong experience with Google Cloud Platform (GCP).
• Proficiency in Python and SQL; experience building distributed data processing and ML pipelines.
• Understanding of MLOps concepts, model lifecycle management, and deployment strategies.

Responsibilities

• Deploy, manage, and monitor production ML models and pipelines.
• Design and maintain automated ML pipelines for batch and real-time scoring.
• Manage Vertex AI training, model registry, endpoints, and prediction services.
• Collaborate with Data Scientists, Data Engineers, and DevOps to operationalize models.

Skills

Python

SQL

Education

Bachelor's degree in Computer Science, Engineering, Data Science, or related field

Tools

Vertex AI

Dataproc

Apache Spark / PySpark

Apache Iceberg

BigQuery

Cloud Storage (GCS)

Docker

Kubernetes

CI/CD

Infrastructure as Code

Job description

TechDigital Group is seeking a seasoned Machine Learning Engineer to build, deploy, and manage scalable ML solutions on Google Cloud Platform. You will operationalize models developed by data scientists, ensure reliable execution, monitoring, and integration with enterprise data platforms using Vertex AI, Dataproc, Apache Spark, and Iceberg.

You will design end-to-end ML pipelines, manage training and endpoints, implement CI/CD for ML, and collaborate with data engineers, architects, and DevOps