Key Responsibilities :

- Azure ML Platform & Architecture

- Design and implement scalable Azure MLOps architecture for model development, training, deployment, monitoring, and retraining.

- Define enterprise-grade deployment patterns using Azure Machine Learning, Azure Kubernetes Service (AKS), and Azure Container Registry (ACR).

- Architect secure and cost-efficient ML infrastructure aligned with cloud governance and compliance standards.

- Develop reusable deployment frameworks and templates for ML workloads across multiple projects.

- FastAPI to Azure ML Migration

- Analyze existing FastAPI-based machine learning applications and APIs.

- Convert and migrate FastAPI model-serving applications into Azure ML Online Endpoints and Batch Endpoints.

- Refactor model inference services to align with Azure ML deployment standards.

- Optimize API performance, scalability, logging, and monitoring during migration.

- Ensure seamless integration of migrated services with existing business applications and downstream systems.

Model Deployment & Lifecycle Management :

- Deploy, manage, and monitor machine learning models using Azure Machine Learning Services.

- Implement model versioning, model registry management, and release governance.

- Automate model packaging, validation, deployment, rollback, and promotion across environments.

- Establish strategies for model retraining, drift detection, and performance monitoring.

CI/CD & Infrastructure Automation :

- Build and maintain CI/CD pipelines using Azure DevOps, GitHub Actions, or GitLab CI.

- Automate infrastructure provisioning using Terraform, ARM Templates, or Bicep.

- Implement Infrastructure-as-Code (IaC) practices for Azure ML environments.

- Automate deployment workflows for ML models, containers, and APIs.

Containerization & Orchestration :

- Containerize ML applications using Docker.

- Deploy and manage workloads on Azure Kubernetes Service (AKS).

- Optimize container performance, scaling policies, and resource utilization.

- Implement secure container deployment practices and vulnerability management.

Monitoring & Governance :

- Implement end-to-end monitoring using Azure Monitor, Application Insights, Log Analytics, Prometheus, and Grafana.

- Establish model observability frameworks for drift detection, data quality monitoring, and prediction tracking.

- Ensure governance, auditability, and compliance for AI/ML systems.