MLOps Engineer | Google Cloud Platform | Vertex AI | Kubeflow

  • Posted 8 hours ago | Updated 8 hours ago

Overview

Remote
$80 - $100
Full Time
Accepts corp to corp applications
Able to Provide Sponsorship

Skills

Agile
Google Cloud
Google Cloud Platform
devops
Machine Learning (ML)
Machine Learning Operations (ML Ops)

Job Details

We are seeking a skilled MLOps Engineer with hands-on experience in Google Cloud Platform (Google Cloud Platform) to design, build, and manage robust ML pipelines and model deployment frameworks. The ideal candidate will have a solid foundation in both machine learning operations and cloud-native tools, enabling seamless model integration, monitoring, and CI/CD workflows.

Key Responsibilities:

  • Design, develop, and manage ML pipelines using Google Cloud Platform tools (Vertex AI, Kubeflow, AI Platform).

  • Automate the training, testing, deployment, and monitoring of ML models.

  • Implement CI/CD pipelines for ML using Cloud Build, GitHub Actions, Jenkins, etc.

  • Manage and monitor model performance and data drift in production.

  • Collaborate with data scientists to productionize models.

  • Implement versioning for models, datasets, and pipeline components.

  • Ensure ML systems' scalability, reliability, and security in Google Cloud Platform.

  • Leverage Terraform / Infrastructure as Code for environment setup and scaling.

  • Apply MLOps best practices to streamline experimentation and reproducibility.

Required Skills:

  • 3+ years of hands-on MLOps experience (total 5+ years in ML/Data Engineering).

  • Strong experience with Google Cloud Platform (Google Cloud Platform) services: Vertex AI, GCS, BigQuery, Cloud Functions, Pub/Sub.

  • Experience with ML tools: Kubeflow, TensorFlow Extended (TFX), MLflow, or similar.

  • Expertise in Docker, Kubernetes, and CI/CD pipelines.

  • Proficiency in Python (especially for automation and scripting).

  • Familiarity with Git, model versioning, and collaborative development practices.

  • Knowledge of monitoring/logging tools (e.g., Stackdriver, Prometheus, Grafana).

Preferred Qualifications:

  • Google Cloud Platform certification (e.g., Professional Machine Learning Engineer or Cloud DevOps Engineer).

  • Prior experience with feature stores, data versioning (DVC), or model registries.

  • Experience working in Agile/Scrum environments.

  • Exposure to data privacy and governance in ML systems.

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