MLOps Engineering Specialist

Job description

Recruiter: Daniel McCarthy Career Grade: D: Internal Closing Date: 16/2/26: Why this job matters: We are looking for an AWS MLOps Engineering Specialist to deploy and operate production grade machine learning platforms using the AWS SageMaker MLOps framework. This role focuses on enabling the full ML lifecycle—data preparation, model deployment, monitoring, and retraining—through standardised, automated, and governed pipelines. You will work at the intersection of data science, cloud engineering, and DevOps, ensuring models built in SageMaker can be reliably deployed at scale, monitored for drift and performance, and governed in line with enterprise and regulatory expectations. You will play a key role in standardising ML lifecycle practices, automating pipelines, and embedding operational excellence, security, and cost efficiency into AI/ML workloads. What you’ll be doing: 7. Define standards for documentation, change management and quality gates that reduce MTTR and improve platform reliability. 8. Partner with data scientists to productionise notebooks and experiments into managed pipelines. 9. Build scalable inference solutions using SageMaker real time and serverless endpoints...