ML Engineer (Azure ML)

Company Overview

(Company information is not specified.)

Job Summary

The ML Engineer (Azure ML) plays a crucial role in designing, deploying, and managing machine learning models using Azure technologies. This position focuses on leveraging Azure ML Studio and other Azure services to create efficient data processing and inference pipelines. The role contributes to the organization’s goals by ensuring high-quality machine learning systems and effective model monitoring.

Responsibilities

• Design and implement end-to-end data pipelines for training and inference using Azure ML.
• Register and deploy machine learning, artificial intelligence (AI), and Generative AI (GenAI) models through Azure ML Studio.
• Utilize Azure Kubernetes Service (AKS) for deploying models and managing their life cycle.
• Develop and maintain monitoring pipelines to ensure optimal model performance and detect drift.
• Implement orchestration of machine learning pipelines by applying MLOps best practices.
• Collaborate with cross-functional teams to ensure that the models are deployed using best practices in coding and environment setup.

Qualifications

• Strong experience with Azure technologies, including Azure ML Studio, AKS, Blob Storage, Azure Data Factory (ADF), and Azure DevOps (ADO) Pipelines.
• Excellent skills in Python, with a focus on coding best practices, environment setup, and dependency management.
• Proficient in using MLflow for model experimentation, logging artifacts, and monitoring.
• Knowledge of model monitoring techniques, particularly drift detection and performance monitoring.
• Familiarity with DevOps practices, specifically Continuous Integration/Continuous Deployment (CI/CD) using Azure DevOps and Git.
• Understanding of fundamental Data Engineering concepts.
• Docker-based deployment experience is a plus.

Preferred Skills

• Experience with automatic code review tools, such as Linting and Black.
• Familiarity with orchestrating machine learning pipelines and ensuring compliance with best practices in MLOps.

Experience

(Experience requirements are not specified, but the role typically requires relevant industry experience in machine learning and Azure technologies.)

Environment

(Working environment details are not specified.)

Salary

(Salary range is not specified.)

Growth Opportunities

(Potential career advancement opportunities within the company are not specified.)

Benefits

(Benefits information is not specified.)