MLOps Engineer Google Cloud Platform & Vertex AI

  • Atlanta, GA
  • Posted 8 hours ago | Updated 1 hour ago

Overview

Hybrid
$80 - $100
Contract - W2
Contract - Independent
Contract - 36 Month(s)
Able to Provide Sponsorship

Skills

DevOps
Data Governance
Data Engineering
Continuous Integration
Artificial Intelligence
Continuous Delivery
Machine Learning Operations (ML Ops)
Machine Learning (ML)

Job Details

We are hiring an experienced MLOps Engineer with hands-on expertise in Google Cloud Platform (Google Cloud Platform) and Vertex AI. You ll be responsible for building and maintaining scalable machine learning infrastructure, automating workflows, and enabling robust AI/ML deployments in production environments.

Key Responsibilities:

  • Develop, automate, and manage ML pipelines using Vertex AI Pipelines, Kubeflow, and Cloud Composer

  • Deploy and monitor models in production using Vertex AI and CI/CD workflows (Cloud Build, GitHub Actions, etc.)

  • Work closely with ML engineers and data scientists to productionize models and manage model versioning, retraining, and rollback strategies

  • Manage infrastructure-as-code using Terraform, Deployment Manager, or similar tools

  • Implement observability and monitoring (logging, metrics, alerts) using Cloud Monitoring, Prometheus, or Grafana

  • Ensure security, governance, and compliance of ML workflows within the Google Cloud Platform ecosystem

  • Optimize cost, performance, and scalability of ML systems in production

Required Skills:

  • 5+ years in DevOps/MLOps or Cloud ML Engineering, with recent Google Cloud Platform production experience

  • Strong hands-on experience with Vertex AI, Cloud Functions, BigQuery, and GCS

  • Proficiency with tools like TFX, Kubeflow, Docker, and Kubernetes (GKE preferred)

  • Expertise in CI/CD, GitOps, and workflow orchestration

  • Programming skills in Python (ML workflows) and Bash/Terraform (infra scripting)

  • Solid understanding of model lifecycle, pipeline automation, and ML monitoring

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

Nice to Have:

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

  • Familiarity with LLMs, RAG, or Vertex AI Search & Conversation

  • Experience with multi-region deployments or hybrid cloud setups

  • Exposure to Data Governance and Responsible AI practices

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.

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