Senior Software Engineer - ML Ops

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

• Preferred 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).
• 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%