Negotiable
Undetermined
Undetermined
London Area, United Kingdom
Summary: The Machine Learning Engineer role in London requires a professional with over 7 years of experience, focusing on designing and implementing data pipelines using AWS technologies. The position emphasizes hands-on experience with AWS S3, SageMaker, and real-time streaming systems. The engineer will be responsible for developing and deploying machine learning models and managing data workflows. This contract role is ideal for candidates with a strong background in AWS-based data engineering and machine learning solutions.
Key Responsibilities:
- Design and implement S3-based data pipelines
- Build and manage real-time micro-batch pipelines (5 mins / hourly / daily)
- Set up and manage Redis clusters
- Develop Kafka / Flink streaming pipelines
- Work with MongoDB / Atlas (alternative data implementation)
- Implement SageMaker MLOps, model training & deployment
- Develop and deploy ML models using PyTorch
Key Skills:
- Strong hands-on experience in AWS S3 and AWS SageMaker
- Strong experience in AWS-based data engineering and ML solutions
- Good understanding of real-time streaming and distributed systems
Salary (Rate): undetermined
City: London
Country: United Kingdom
Working Arrangements: undetermined
IR35 Status: undetermined
Seniority Level: undetermined
Industry: IT
Job Title - Machine Learning Engineer
Location - London, UK
Experience - 7+ Years
Job Type - Contract
Key Skills (Mandatory): Strong hands-on experience in AWS S3 and AWS SageMaker
Core Responsibilities:
- Design and implement S3-based data pipelines
- Build and manage real-time micro-batch pipelines (5 mins / hourly / daily)
- Set up and manage Redis clusters
- Develop Kafka / Flink streaming pipelines
- Work with MongoDB / Atlas (alternative data implementation)
- Implement SageMaker MLOps, model training & deployment
- Develop and deploy ML models using PyTorch
Experience:
Strong experience in AWS-based data engineering and ML solutions
Good understanding of real-time streaming and distributed systems