Work Flexibility: Hybrid

What You Will Do:

· Design, build, and maintain end-to-end MLOps pipelines for ML model training, testing, and deployment.

· Collaborate with Data Scientists to productionize ML models in Azure ML and Azure Databricks.

· Implement CI/CD pipelines for ML workflows using Azure DevOps, GitHub Actions, or Jenkins.

· Automate infrastructure provisioning using IaC tools (Terraform, ARM templates, or Bicep).

· Monitor and manage deployed models using Azure Monitor, Application Insights, and MLflow.

· Implement best practices in model versioning, model registry, experiment tracking, and artifact management.

· Ensure security, compliance, and cost optimization of ML solutions deployed on Azure.

· Work with cross-functional teams (Data Engineers, DevOps Engineers, Data Scientists) to streamline ML delivery.

· Develop monitoring/alerting for ML model drift, data drift, and performance degradation.

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What You Need:

Required Skills

· 5-10 years of experience in programming: Python (must), SQL;. MLOps/DevOps Tools: MLflow, Azure DevOps, GitHub Actions, Docker, Kubernetes (AKS).

· Azure Services: Azure ML, Azure Databricks, Azure Data Factory, Azure Storage, Azure Functions, Azure Event Hubs.

· CI/CD: Experience designing pipelines for ML workflows. IaC: Terraform, ARM templates, or Bicep.

· Data Handling: Experience with Azure Data Lake, Blob Storage, and Synapse Analytics.

· Monitoring & Logging: Azure Monitor, Prometheus/Grafana, Application Insights. Strong knowledge of ML lifecycle (data preprocessing, model training, deployment, monitoring).

Preferred Skills:

· Experience with Azure Kubernetes Service (AKS) for scalable model deployment.

· Knowledge of feature stores and distributed training frameworks. Familiarity with RAG (Retrieval Augmented Generation) pipelines and LLMOps.

· Azure certifications such as Azure AI Engineer Associate, Azure Data Scientist Associate, or Azure DevOps Engineer Expert.

Travel Percentage: 10%