Hybrid Cloud MLOps Architect Job at Openkyber, Georgia

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  • Openkyber
  • Georgia

Job Description

Job Title: Hybrid Cloud MLOps Architect

Location: Hybrid / Remote
Duration: Long Term

Role Overview

We are seeking a Hybrid Cloud MLOps Architect to design, troubleshoot, and optimize ML infrastructure, pipelines, and cloud integrations across multi-cloud environments. The ideal candidate will architect end-to-end MLOps frameworks across Windows, Linux, Azure, and Google Cloud Platform , ensuring resilient, scalable, and well-governed machine learning operations. This role involves deep technical troubleshooting, workflow optimization, and collaboration with cross-functional data, ML, and platform engineering teams.

Key Responsibilities Architecture & Platform Engineering
  • Architect and optimize hybrid cloud MLOps platforms spanning on-prem, Azure, and Google Cloud Platform.

  • Develop, test, and refine ML workflows, pipelines, and orchestration patterns for training, deployment, and monitoring.

  • Ensure end-to-end operational reliability for ML systems through robust pipeline design, governance, and automation.

Troubleshooting & Optimization
  • Diagnose and resolve complex issues in ML pipelines, model deployments, and cloud-native compute environments.

  • Troubleshoot Linux/Windows-based ML runtime environments and optimize compute/storage performance.

  • Analyze logs, workflows, and cloud integration points to maintain high availability and reliability.

Hybrid Cloud Integration
  • Integrate ML systems with cloud services (Azure ML, Google Cloud Vertex AI, containerized workloads).

  • Support cross-cloud infrastructure discovery, configuration, environment validation, and interoperability.

Collaboration & Documentation
  • Work closely with Data Science, DevOps, and Cloud Architecture teams to ensure accurate mapping of ML workflows to infrastructure.

  • Maintain detailed documentation of MLOps architectures, workflows, troubleshooting steps, and improvement plans.

  • Stay current with best practices in MLOps, cloud services, observability, and ML governance.

Requirements
  • Proven experience designing and operating hybrid or multi-cloud MLOps architectures .

  • Strong expertise with Linux and Windows-based ML environments .

  • Experience in pipeline debugging, workflow analysis, and cloud integration troubleshooting.

  • Hands-on experience with Azure and Google Cloud Platform services.

  • Proficiency with Regex , logs analysis, and technical debugging.

  • Excellent analytical, troubleshooting, and communication skills.

Preferred Qualifications
  • Experience with ML pipeline orchestration (Kubeflow, Airflow, MLflow, Vertex Pipelines, Azure ML pipelines).

  • Familiarity with ITSM/ITIL concepts for operational workflow governance.

  • Scripting skills in Python, Bash, or PowerShell for automation and pipeline enhancements.

  • Exposure to hybrid cloud architectures, containerization, and distributed compute.

Job Tags

Remote work,

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