Description :

About the Role :

The ML Engineer (Azure ML) will be responsible for designing, developing, and deploying end-to-end Machine Learning and Generative AI solutions within the Azure ecosystem.

The role requires deep technical expertise in Azure ML Studio, model lifecycle management, and operationalization of ML workflows using MLOps principles.

The ideal candidate should be proficient in Python-based model development, CI/CD integration, and containerized deployments using Azure Kubernetes Service (AKS).

This position demands hands-on experience in building production-ready ML pipelines, managing data flow with Azure Data Factory, and implementing scalable and secure deployment strategies.

Key Responsibilities :

- Design, train, and deploy ML and GenAI models using Azure ML Studio and integrated services.

- Develop and operationalize MLOps pipelines using Azure DevOps (ADO) and CI/CD workflows.

- Deploy scalable containerized models on Azure Kubernetes Service (AKS) with automated scaling and monitoring.

- Manage data ingestion, transformation, and orchestration using Azure Data Factory (ADF) and Blob Storage.

- Implement version control, model tracking, and experiment management using MLflow or equivalent tools.

- Collaborate with data scientists, product engineers, and cloud architects to streamline model deployment and monitoring.

- Optimize model performance, latency, and cost by leveraging Azure Compute and Storage configurations.

- Integrate deployed models with business applications via REST APIs or event-based systems.

- Develop and maintain reusable MLOps templates, infrastructure scripts, and deployment automation scripts.

- Ensure compliance, security, and governance across ML pipelines and model lifecycles.

Requirements :

- 4 to 9 years of experience in building and deploying ML models in production environments.

- Hands-on experience with Azure ML Studio, Azure Data Factory, and Azure DevOps pipelines.

- Proficiency in model development and automation using Python, MLflow, and MLOps frameworks.

- Strong knowledge of deploying ML models on AKS with Docker-based containerization.

- Experience managing large-scale data pipelines and model artifacts in Azure Blob Storage.

- Familiarity with CI/CD implementation for ML workflows, including testing and rollback strategies.

- Working understanding of Generative AI model deployment and serving on cloud infrastructure.

- Ability to monitor, retrain, and optimize models post-deployment for reliability and scalability.

- Strong command over model lifecycle governance, reproducibility, and environment standardization