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3 weeks ago
Key Responsibilities: Design, build, and maintain robust MLOps pipelines supporting model training, validation, deployment, and monitoring.
Use MLflow for experiment tracking, model versioning, and registry.
Develop orchestrated ML workflows with Apache Airflow or Kubeflow Pipelines.
Manage and deploy containerized workloads using Kubernetes.
Implement Infrastructure-as-Code via Terraform.
Build and maintain CI/CD pipelines using Azure Dev Ops.
Collaborate with Data Scientists and Engineers to automate ML processes.
Utilize Azure ML for managed model training and deployment.
Work with Databricks for scalable data processing and collaborative development.
Must-Have Skills:2+ years in MLOps, Dev Ops, or a related role.
Hands-on with MLflow, Airflow or Kubeflow.
Strong in Kubernetes, Terraform, and Azure Dev Ops.
Experience with Azure ML and Databricks.
Proficient in scripting languages like Python, Bash, or Power Shell.
Solid grasp of CI/CD, Docker, and ML lifecycle management.
Nice-to-Have: Agile/Scrum environment experience.
Strong problem-solving, documentation, and communication skills.
Cloud certifications such as Microsoft Certified: Azure AI Engineer or Azure Dev Ops Engineer.
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