ML Data Engineer
Posted 1 week ago by Experis UK
Negotiable
Inside
Hybrid
Knutsford, England, United Kingdom
Summary: The ML Data Engineer role involves developing and managing AWS-based machine learning pipelines and cloud-native deployments. The position requires expertise in building scalable data workflows and deploying ML models while overseeing the entire AI lifecycle in production settings. The role is hybrid, based in Knutsford, and emphasizes collaboration with data scientists and engineers. The ideal candidate will have strong experience in MLOps and data engineering technologies.
Key Responsibilities:
- Build and maintain robust data pipelines and ML workflows on AWS
- Develop and deploy machine learning models using SageMaker and MLOps tools
- Implement CI/CD pipelines for automated testing and deployment
- Create lightweight front-end interfaces for model interaction and visualization
- Monitor model performance and ensure reliability in production environments
- Collaborate with data scientists and engineers to streamline the AI lifecycle
Key Skills:
- AWS Data Engineering: ECS, SageMaker, cloud-native data pipelines
- ML Engineering & MLOps: MLflow, Airflow, Docker, Kubernetes
- CI/CD & DevOps: GitLab, Jenkins, automated deployment workflows
- AI Lifecycle Management: Model training, deployment, monitoring
- Front-End Development: HTML, Streamlit, Flask (for lightweight dashboards and interfaces)
- Cloud Model Deployment: Experience deploying and monitoring models in AWS
- Programming & Big Data: Python, PySpark, familiarity with big data ecosystems
- RESTful APIs: Integration of backend services and model endpoints
Salary (Rate): undetermined
City: Knutsford
Country: United Kingdom
Working Arrangements: hybrid
IR35 Status: inside IR35
Seniority Level: undetermined
Industry: IT
Role Title: ML Data Engineer
Start Date: ASAP
End Date: EOY
Location: Knutsford (Hybrid)
Rate: £410p/d via Umbrella
Overview Role Description: We are seeking a highly capable Data & ML Engineer with strong experience in AWS-based machine learning pipelines , MLOps , and cloud-native deployment . This role focuses on building scalable data workflows, deploying ML models, and managing the full AI lifecycle in production environments.
Primary Skills
Key Skills & Technologies
- AWS Data Engineering: ECS, SageMaker, cloud-native data pipelines
- ML Engineering & MLOps: MLflow, Airflow, Docker, Kubernetes
- CI/CD & DevOps: GitLab, Jenkins, automated deployment workflows
- AI Lifecycle Management: Model training, deployment, monitoring
- Front-End Development: HTML, Streamlit, Flask (for lightweight dashboards and interfaces)
- Cloud Model Deployment: Experience deploying and monitoring models in AWS
- Programming & Big Data: Python, PySpark, familiarity with big data ecosystems
Secondary Skills
- RESTful APIs: Integration of backend services and model endpoints
Responsibilities
- Build and maintain robust data pipelines and ML workflows on AWS
- Develop and deploy machine learning models using SageMaker and MLOps tools
- Implement CI/CD pipelines for automated testing and deployment
- Create lightweight front-end interfaces for model interaction and visualization
- Monitor model performance and ensure reliability in production environments
- Collaborate with data scientists and engineers to streamline the AI lifecycle