Machine Learning Engineer
Posted 5 days ago by DRAS CONSULTING LIMITED
Negotiable
Undetermined
Undetermined
London Area, United Kingdom
Summary: The Machine Learning Engineer role focuses on designing, building, and deploying scalable data and machine learning pipelines. The position requires expertise in real-time and batch data processing systems, cloud-based data storage, and MLOps workflows to support production-grade machine learning models. Collaboration with data scientists is essential to ensure the successful productionization of ML models. The role emphasizes the management of high-performance data access and the implementation of robust data processing architectures.
Key Responsibilities:
- Design, set up, and manage Redis clusters for high-performance data access
- Build and maintain Kafka / Flink streaming pipelines for real-time data processing
- Develop and manage S3-based data pipelines for large-scale data storage and processing
- Implement real-time micro-batch processing (5-minute, hourly, and daily jobs)
- Evaluate and implement alternative data storage solutions such as MongoDB / MongoDB Atlas where appropriate
- Build and manage MLOps workflows using AWS SageMaker, including training, model versioning, and deployment
- Deploy and monitor machine learning models in production environments
- Collaborate closely with data scientists to productionize ML models
- Ensure scalability, reliability, and performance of data and ML systems
Key Skills:
- Strong experience with Redis cluster setup and management
- Hands-on experience with Kafka and/or Apache Flink for streaming data pipelines
- Experience building data pipelines using AWS S3
- Knowledge of real-time and micro-batch processing architectures
- Familiarity with MongoDB / MongoDB Atlas as a data storage solution
- Experience with AWS SageMaker (MLOps, training, and model deployment)
- Strong proficiency in PyTorch
- Solid understanding of cloud-native architectures and distributed systems
Salary (Rate): undetermined
City: London Area
Country: United Kingdom
Working Arrangements: undetermined
IR35 Status: undetermined
Seniority Level: undetermined
Industry: IT
Role Overview We are looking for a skilled Machine Learning Engineer to design, build, and deploy scalable data and machine learning pipelines. The role involves working with real-time and batch data processing systems, cloud-based data storage, and end-to-end MLOps workflows to support production-grade machine learning models.
Key Responsibilities
- Design, set up, and manage Redis clusters for high-performance data access
- Build and maintain Kafka / Flink streaming pipelines for real-time data processing
- Develop and manage S3-based data pipelines for large-scale data storage and processing
- Implement real-time micro-batch processing (5-minute, hourly, and daily jobs)
- Evaluate and implement alternative data storage solutions such as MongoDB / MongoDB Atlas where appropriate
- Build and manage MLOps workflows using AWS SageMaker, including training, model versioning, and deployment
- Deploy and monitor machine learning models in production environments
- Collaborate closely with data scientists to productionize ML models
- Ensure scalability, reliability, and performance of data and ML systems
Required Skills & Experience
- Strong experience with Redis cluster setup and management
- Hands-on experience with Kafka and/or Apache Flink for streaming data pipelines
- Experience building data pipelines using AWS S3
- Knowledge of real-time and micro-batch processing architectures
- Familiarity with MongoDB / MongoDB Atlas as a data storage solution
- Experience with AWS SageMaker (MLOps, training, and model deployment)
- Strong proficiency in PyTorch
- Solid understanding of cloud-native architectures and distributed systems
Nice to Have
- Experience with large-scale production ML systems
- Knowledge of data governance, monitoring, and logging in ML pipelines
- Familiarity with CI/CD for ML workflows