Job Description

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.

• 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.

• 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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