Google Cloud Platform ML Ops Engineer Vertex AI Expert

  • Posted 12 hours ago | Updated 12 hours ago

Overview

Remote
$70 - $80
Accepts corp to corp applications
Contract - Independent
Contract - 36 Month(s)
Able to Provide Sponsorship

Skills

Agile
Artificial Intelligence
Cloud Computing
Collaboration
Continuous Delivery
Continuous Integration
Data Engineering
DevOps
Docker
FOCUS
GitLab
Good Clinical Practice
Google Cloud
Google Cloud Platform
Jenkins
Lifecycle Management
Machine Learning (ML)
Machine Learning Operations (ML Ops)
Management
Performance Monitoring
Python
Scalability
Scrum
Stackdriver
TensorFlow
Terraform
Training
Vertex
Workflow
Data Flow

Job Details

We are seeking a highly skilled Google Cloud Platform Expert with a strong background in Machine Learning Operations (ML Ops) and model deployment using Vertex AI. The ideal candidate will have deep hands-on experience with the Google Cloud Platform, and a proven track record of building scalable ML pipelines, automating deployments, and managing model lifecycle on Vertex AI.

Key Responsibilities:

  • Design, develop, and deploy ML pipelines using Google Cloud Platform tools and services.

  • Lead ML Ops workflows including model training, validation, deployment, monitoring, and retraining using Vertex AI.

  • Integrate CI/CD pipelines for ML models.

  • Collaborate with data scientists, ML engineers, and DevOps teams to operationalize machine learning models.

  • Implement model versioning, performance monitoring, and rollback mechanisms.

  • Ensure scalability, performance, and security of ML infrastructure on Google Cloud Platform.

Required Skills & Experience:

  • 5+ years of experience in Google Cloud Platform with a strong focus on ML and Data Engineering.

  • Expertise in Vertex AI for model training, deployment, and lifecycle management.

  • Hands-on experience with Kubeflow, TensorFlow, Cloud Functions, Cloud Run, or AI Platform.

  • Strong knowledge of Python, Docker, and Terraform.

  • Experience with CI/CD tools like Jenkins, GitLab CI/CD, or Cloud Build.

  • Solid understanding of ML Ops concepts and best practices.

  • Familiarity with monitoring tools like Prometheus, Stackdriver, or Cloud Monitoring.

Preferred Qualifications:

  • Google Cloud Platform Professional Machine Learning Engineer or Google Cloud Platform Cloud Engineer certification.

  • Experience with data pipelines using BigQuery, Dataflow, or Dataproc.

  • Experience working in Agile/Scrum environments.

How to Apply:
If you meet the qualifications and are interested in this exciting opportunity, please share your resume and availability.
Or connect directly on linkedin.com/in/talentdelivery-msp-workforcemodernization/

Employers have access to artificial intelligence language tools (“AI”) that help generate and enhance job descriptions and AI may have been used to create this description. The position description has been reviewed for accuracy and Dice believes it to correctly reflect the job opportunity.

About Hexacorp