Azure MLOps Engineer: Scalable ML Infra & Automation

Gazelle Global; Wokingham; On-site; GBP 70,000 - 110,000; Full time

Job summary

Gazelle Global is seeking an Azure MLOps engineer to build and maintain scalable cloud ML infrastructure on Azure. You will collaborate with data scientists, forecasters and developers to deploy models and ensure reliable, secure operation at terabyte-scale data processing.

The role focuses on automating deployment, containerization with Docker, and implementing monitoring, logging, and auto-scaling. Strong Azure ML experience and Python skills are essential for success.

Qualifications

• 5+ years of experience in MLOps, DevOps or a related field.
• Strong understanding of machine learning principles and model lifecycle management.
• Experience with Azure Machine Learning services.
• Proficiency in Python and ML frameworks (TensorFlow, PyTorch, Scikit-learn).
• Experience with Docker and containerization for model deployment.
• Familiarity with data formats Parquet, JSON, GRIP and NETCDF.
• Knowledge of data pipelines, storage, and data protection best practices.

Responsibilities

• Collaborate with data scientists/forecaster to deploy ML models into production environments.
• Follow deployment strategies to ensure safe, controlled rollouts.
• Design and manage infrastructure for hosting ML models on Azure.
• Utilize Docker to package models and dependencies.
• Establish Azure monitoring and logging for deployed models.
• Continuously monitor and maintain models in production for performance.
• Optimize ML infrastructure for scalability and cost-efficiency.
• Implement auto-scaling to handle varying workloads.
• Enforce security best practices to protect models and data.
• Ensure compliance with regulations and data protection standards.
• Oversee data pipelines and storage for training and inference.
• Implement data versioning and lineage tracking.
• Collaborate with data scientists and developers on requirements.
• Coordinate with DevOps to align MLOps with goals.
• Continuously optimize ML models for performance.
• Identify bottlenecks and improve system efficiency.
• Maintain documentation for deployments, configs and architecture.

Skills

MLOps

Azure

Automation

Python

DevOps

Docker

Tools

Azure Machine Learning

TensorFlow

PyTorch

Scikit-learn

Azure DevOps

SQL

NoSQL

Parquet

JSON

GRIP

NETCDF

Job description

Gazelle Global is seeking an Azure MLOps engineer to build and maintain scalable cloud ML infrastructure on Azure. You will collaborate with data scientists, forecasters and developers to deploy models and ensure reliable, secure operation at terabyte-scale data processing.

The role focuses on automating deployment, containerization with Docker, and implementing monitoring, logging, and auto-scaling. Strong Azure ML experience and Python skills are essential for success.