Location: Hybrid / Remote
Duration: Long Term
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 EngineeringArchitect 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.
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.
Integrate ML systems with cloud services (Azure ML, Google Cloud Vertex AI, containerized workloads).
Support cross-cloud infrastructure discovery, configuration, environment validation, and interoperability.
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.
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.
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.
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