MLOPs Engineer with Azure

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

We're looking for a skilled Azure MLOps engineer with a focus on automation to join our rapidly growing team. Your role will involve implementing, and maintaining scalable, secure cloud MLOps infrastructure on Azure, while ensuring the infrastructure's reliability and availability for data processing in the terabyte scale. You will collaborate with our Architects, DevOps consultant, Data scientist, forecaster and developers to provide a robust platform for our innovative applications.

Your responsibilities:

• Collaborate with data scientists/forecaster to deploy machine learning models into production environments.
• Follow deployment strategies in place to ensure safe and controlled rollouts.
• Design and manage the infrastructure required for hosting ML models, including Azure cloud resources.
• Utilize containerization technologies like Docker to package models and dependencies.
• Establish Azure monitoring solutions to track the performance and health of deployed models. Set up logging mechanisms to capture relevant information for debugging and auditing purposes.
• Continuously monitor and maintain models in production, ensuring optimal performance, accuracy and reliability.
• Optimize ML infrastructure for scalability and cost-effectiveness.
• Implement auto-scaling mechanisms to handle varying workloads efficiently such as parallel run
• Enforce security best practices to safeguard both the models and the data they process.
• Ensure compliance with industry regulations and data protection standards.
• Oversee the management of data pipelines and data storage systems required for model training and inference.
• Implement data versioning and lineage tracking to maintain data integrity.
• Work closely with data scientists, software engineers, and other stakeholders to understand model requirements and system constraints.
• Collaborate with DevOps teams to align MLOps practices with broader organizational goals.
• Continuously optimize and fine-tune ML models for better performance.
• Identify and address bottlenecks in the system to enhance overall efficiency.
• Maintain comprehensive documentation for deployment processes, configurations, and system architecture.

Communicate effectively with non-technical stakeholders, providing insights into the performance and impact of ML models

Your Profile

Essential skills/knowledge/experience: 5+ Years of Experience.

• 5+ years of experience in MLOps, DevOps or a related field.
• Strong understanding of machine learning principles and model lifecycle management.
• Passionate about making things work iteratively and automating + scaling them
• Deep knowledge of software development and engineering in combination with ML models
• Experience in development Azure Machine Learning or any MLOPs frameworks
• Experience with SQL and noSQL environments, Azure SQL database and Storage Account – blob is must
• Proficiency in programming languages such as Python, with hands-on experience in machine learning frameworks like TensorFlow, PyTorch, or Scikit-learn.
• Experience with cloud platforms Azure machine learning services.
• Experience with monitoring tools and practices for model performance in production.
• practical ability in creating build and release pipelines in Azure DevOps for ML artifacts
• experience in supporting real-time-inference scenarios with Azure Machine Learning
• Knowledge of tools, methods, and frameworks used by data scientists
• Familiarity with data engineering practices and tools.
• Familiarity with data formats such as GRIP, NETCDF, Parquet, and JSON is a plus.
• Azure data scientist associate certificate is plus