Overview
On Site
BASED ON EXPERIENCE
Full Time
Contract - W2
Contract - Independent
Contract - 7+ mo(s)
Skills
MLOPS
ML OPS
GCP
GOOGLE CLOUD
GOOGLE ML
GOOGLE MACHINE LEARNING
DATA LAKE
DATALAKE
Job Details
ML Ops Engineer
Location: Pleasanton US Remote
* Support and maintain ML pipelines, workflows, and deployments on Google Machine Learning and related Google services.
* Monitor production ML models for performance, accuracy, and data drift, initiating retraining or fixes as needed.
* Troubleshoot issues in Google ML environments, including compute, storage, networking, and service integrations.
* Manage and optimize Azure resources such as Data Lake, Blob Storage, Kubernetes Service (AKS), and Databricks for ML workloads.
* Implement CI/CD pipelines for ML model deployment using Google DevOps, GitHub Actions, or similar tools.
* Ensure adherence to data security, compliance, and governance policies within Google s environment.
* Collaborate with data scientists, data engineers, and developers to resolve operational bottlenecks.
* Document operational procedures, incident resolutions, and best practices for ML Ops support.
Location: Pleasanton US Remote
JOB DESCRIPTION:
* Support and maintain ML pipelines, workflows, and deployments on Google Machine Learning and related Google services.
* Monitor production ML models for performance, accuracy, and data drift, initiating retraining or fixes as needed.
* Troubleshoot issues in Google ML environments, including compute, storage, networking, and service integrations.
* Manage and optimize Azure resources such as Data Lake, Blob Storage, Kubernetes Service (AKS), and Databricks for ML workloads.
* Implement CI/CD pipelines for ML model deployment using Google DevOps, GitHub Actions, or similar tools.
* Ensure adherence to data security, compliance, and governance policies within Google s environment.
* Collaborate with data scientists, data engineers, and developers to resolve operational bottlenecks.
* Document operational procedures, incident resolutions, and best practices for ML Ops support.
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