ML Ops Engineer – AWS SageMaker
Posted 1 day ago by BrightBox Group
£550 Per day
Inside
Remote
Newcastle Upon Tyne, England, United Kingdom
Summary: The MLOps Engineer role focuses on leveraging AWS SageMaker to support the deployment and operationalization of machine learning models within an 8-week contract. The position requires an experienced professional who can quickly adapt to a fast-paced environment and contribute to the design and maintenance of MLOps pipelines. The role also involves collaboration with data scientists and engineering teams to ensure the reliability and scalability of ML systems. Security clearance is a prerequisite for this position.
Key Responsibilities:
- Design, build, and maintain MLOps pipelines using AWS SageMaker
- Deploy, monitor, and manage machine learning models in production
- Automate model training, testing, and deployment workflows
- Ensure scalability, reliability, and security of ML systems
- Collaborate with data scientists and engineering teams to productionise models
- Troubleshoot and optimise existing ML pipelines
Key Skills:
- Strong hands-on experience with AWS SageMaker
- Solid understanding of MLOps best practices
- Experience with CI/CD pipelines for ML workloads
- Proficiency with Python and relevant ML frameworks
- Experience working in cloud-based environments (AWS)
- Active SC Clearance
Salary (Rate): £550pd
City: Newcastle Upon Tyne
Country: United Kingdom
Working Arrangements: remote
IR35 Status: inside IR35
Seniority Level: undetermined
Industry: IT
MLOps Engineer – AWS SageMaker
Contract Length: Initial 8-week contract
Location: Remote
Security Clearance: SC Clearance
£500pd - £550pd – (Inside IR35)
Role Overview
We are seeking an experienced MLOps Engineer with strong expertise in AWS SageMaker to support the delivery, deployment, and operationalisation of machine learning models. This is a short-term contract role, ideal for someone who can hit the ground running in a fast-paced environment.
Key Responsibilities
- Design, build, and maintain MLOps pipelines using AWS SageMaker
- Deploy, monitor, and manage machine learning models in production
- Automate model training, testing, and deployment workflows
- Ensure scalability, reliability, and security of ML systems
- Collaborate with data scientists and engineering teams to productionise models
- Troubleshoot and optimise existing ML pipelines
Required Skills & Experience
- Strong hands-on experience with AWS SageMaker
- Solid understanding of MLOps best practices
- Experience with CI/CD pipelines for ML workloads
- Proficiency with Python and relevant ML frameworks
- Experience working in cloud-based environments (AWS)
- Security Requirements
- Active SC Clearance