Machine Learning Engineer
Posted 3 days ago by Signify Technology
£600 Per day
Undetermined
Hybrid
London Area, United Kingdom
Summary: The Machine Learning Engineer role is a 6-month contract position focused on enhancing machine learning systems within a fast-growing technology company. The engineer will be responsible for improving ML retraining and deployment pipelines, collaborating with ML scientists, and optimizing performance and observability of ML systems. The position offers a hybrid or remote working arrangement based in Farringdon, London.
Key Responsibilities:
- Own and improve ML retraining pipelines to reduce manual effort for ML scientists.
- Enhance model deployment and inference pipelines (primarily using AWS SageMaker).
- Improve observability, monitoring, and overall performance of ML systems.
- Work closely with ML scientists to identify pain points and translate them into scalable solutions.
- Optimise asynchronous inference pipelines (Kafka, RabbitMQ).
- Implement features such as shadow deployments, A/B testing, and enhanced metrics.
- Improve CI/CD pipelines to accelerate model iteration and deployment.
- Collaborate within a cross-functional product squad.
Key Skills:
- Strong Python engineering skills.
- Experience with ML training and deployment pipelines.
- Hands-on experience with AWS (ideally SageMaker).
- Experience with Docker and containerisation.
- Solid understanding of CI/CD processes.
- Experience with Kafka or similar asynchronous systems (e.g. RabbitMQ).
- Ability to work independently and drive engineering improvements.
- Experience with LLMs, text-based models, or detection systems is a plus.
Salary (Rate): £600/day
City: London
Country: United Kingdom
Working Arrangements: hybrid
IR35 Status: undetermined
Seniority Level: undetermined
Industry: IT
Job title: Machine Learning Engineer (Contract)
Job type: Contract
Contract Length: 6 months
Rate: £600/day+
Role Location: Hybrid or remote (Farringdon, London)
The company: A fast-growing, product-focused technology company operating a large-scale, data-driven platform. The business places a strong emphasis on machine learning to enhance user experience and platform safety, with a collaborative, cross-functional engineering culture.
Role and Responsibilities:
- Own and improve ML retraining pipelines to reduce manual effort for ML scientists.
- Enhance model deployment and inference pipelines (primarily using AWS SageMaker).
- Improve observability, monitoring, and overall performance of ML systems.
- Work closely with ML scientists to identify pain points and translate them into scalable solutions.
- Optimise asynchronous inference pipelines (Kafka, RabbitMQ).
- Implement features such as shadow deployments, A/B testing, and enhanced metrics.
- Improve CI/CD pipelines to accelerate model iteration and deployment.
- Collaborate within a cross-functional product squad.
Job Requirements :
- Strong Python engineering skills.
- Experience with ML training and deployment pipelines.
- Hands-on experience with AWS (ideally SageMaker).
- Experience with Docker and containerisation.
- Solid understanding of CI/CD processes.
- Experience with Kafka or similar asynchronous systems (e.g. RabbitMQ).
- Ability to work independently and drive engineering improvements.
- Experience with LLMs, text-based models, or detection systems is a plus.
Accessibility Statement: We make an active choice to be inclusive towards everyone every day. Please let us know if you require any accessibility adjustments through the application or interview process.