ML Data Engineer

Summary: The ML Data Engineer role involves developing and managing AWS-based machine learning pipelines and MLOps, focusing on scalable data workflows and the AI lifecycle in production. The position requires expertise in cloud-native deployment and collaboration with data scientists and engineers. The role is hybrid, based in Knutsford, and offers a competitive daily rate. The ideal candidate will have a strong background in data engineering and machine learning 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): £410 per day

City: Knutsford

Country: United Kingdom

Working Arrangements: hybrid

IR35 Status: inside IR35

Seniority Level: Mid-Level

Industry: IT

Detailed Description From Employer:

Role Title: ML Data Engineer
Start Date: ASAP
End Date: EOY
Location: Knutsford (Hybrid)
Rate: £410p/d via Umbrella
Role Description:

Overview
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.
Key Skills & Technologies
Primary 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

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