ML Engineer (Azure ML)

Knowledge of DevOps with CI/CD, specifically with Git in Azure Devops

Familiarity with model monitoring including drift detection and performance monitoring

Fundamentals of Data engineering

Experience in Docker based deployment would be beneficial Role Overview: You should have solid experience in Azure, particularly in Azure ML Studio, AKS, Blob storage, ADF, ADO Pipelines. Your role will involve registering and deploying ML/AI/GenAI models via Azure ML Studio, as well as deploying models in AKS Clusters. You will also be required to design and implement data processing, training, inference, and monitoring pipelines using Azure ML. Excellent Python skills are a must, including environment setup, dependency management, coding as per best practices, and knowledge of automatic code review tools like Linting and Black. Experience with MLflow for model experiments, logging artifacts and models, and monitoring is highly desirable. Key Responsibilities:

Register and deploy ML/AI/GenAI models via Azure ML Studio

Deploy models in AKS Clusters

Design and implement data processing, training, inference, monitoring pipelines using Azure ML

Demonstrate excellent Python skills, including environment setup, dependency management, and coding as per best practices

Use automatic code review tools like Linting and Black

Utilize MLflow for model experiments, logging artifacts and models, and monitoring

Orchestrate machine learning pipelines using MLOPs best practices

Apply DevOps with CI/CD knowledge, particularly with Git in Azure Devops

Monitor models for drift detection and performance monitoring

Understand the fundamentals of Data engineering

Experience in Docker based deployment is considered an advantage