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-productive 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.
- Drive best practices for Responsible AI and model explainability.

Leadership &

- Collaboration :
- Mentor and guide junior MLOps and Cloud Engineers.
- Collaborate with Data Scientists and Software Engineers to productionize ML solutions.
- Lead architecture reviews and technical decision-making for AI platforms.
- Maintain technical documentation, deployment standards, and operational runbooks.

Required Technical Skills :

Azure Services :

- Azure Machine Learning (Azure ML), Azure Kubernetes Service (AKS), Azure Container Registry (ACR), Azure DevOps, Azure Monitor, Application Insights, Azure Storage Services, Azure Key Vault, Azure Functions (preferred), Azure Data Factory (preferred).

MLOps &

- Deployment :
- Azure ML Online Endpoints, Azure ML Batch Endpoints, Model Registry Management, MLflow, Model Monitoring &
- Drift Detection, Feature Store concepts.

Programming :

- Python (Expert), FastAPI, Bash/Shell Scripting, REST APIs, Microservices Architecture.

Containerization &

- Infrastructure :
- Docker, Kubernetes, AKS, Terraform, ARM Templates / Bicep, Git &
- Version Control.

Monitoring &

- Observability :
- Azure Monitor, Application Insights, Prometheus, Grafana, ELK Stack.

📌 MLOps Engineer - Azure Storage Management (India)
🏢 Ayno Technologies
📍 India