MLOps Engineer

Project - the aim you'll have

Our client is pioneering personalized wellness through innovative technology. Their team is committed to enhancing healthcare experiences using cutting-edge AI. They value creativity, collaboration, and the drive to revolutionize personal wellness. Their culture thrives on innovation, continuous improvement, and pushing boundaries. Join their dynamic and rapidly growing team and be part of a journey that's reshaping the future of healthcare.

As an MLOps Engineer, you will be instrumental in designing and implementing the infrastructure required to support machine learning systems. You will focus on ensuring the reliability, scalability, and efficiency of ML models and data pipelines within our Azure cloud environment.

Position - how you'll contribute

• Design and implement robust data pipelines and engineering infrastructure to support machine learning systems at scale.
• Develop and deploy scalable tools and services to handle machine learning training and inference.
• Build and maintain systems for the ML development lifecycle, continuous training, real-time feature stores, and model deployment.
• Ensure reproducibility, versioning, and data quality throughout the ML lifecycle.
• Collaborate with ML engineers and data engineers to optimize the performance and scalability of our ML models.
• Identify and evaluate new technologies to improve performance, maintainability, and reliability of machine learning systems.
• Communicate with stakeholders to build requirements and track progress.
• Implement monitoring and alerting systems to ensure the reliability and performance of deployed models.

Requirements

Expectations - the experience you need

• BS/MS in Computer Science, Engineering, or a related field.
• 3+ years of experience as a Platform Engineer, ML DevOps Engineer, or Data Engineer, with a focus on machine learning operations.
• Strong programming skills in Python and experience with orchestration tools such as Airflow and MLflow.
• Experience with Azure cloud technologies, including Azure Machine Learning, Azure Databricks, and other relevant services.
• Proficiency in managing and optimizing big data storage and data warehouse solutions.
• Strong understanding of machine learning methodologies and best practices.
• Excellent problem-solving skills and the ability to work in a dynamic and agile environment.
• Strong communication and collaboration skills, with the ability to work effectively across teams. Strong understanding of machine learning methodologies and best practices.
• Excellent problem-solving skills and the ability to work in a dynamic and agile environment.
• Strong communication and collaboration skills, with the ability to work effectively across teams.
• Being up to date with new ML practices such as GenAI, ML Inference, and GPU based training.