Machine Learning Engineer

Job Overview
Job Title: Machine Learning Engineer
Location: North London, United Kingdom
Working Arrangement: Hybrid
Employment Type: Full-Time, 6-Month Contract
Recruitment Company: C4S Search Ltd.
Cloud Platform: Microsoft Azure
Primary Technologies: Python, TensorFlow, PyTorch, MLOps and Recommendation Systems
About The Role
C4S Search Ltd. is seeking a Machine Learning Engineer for a six-month contract opportunity based in North London, with a hybrid working arrangement. This role is suited to an experienced machine learning professional who can design, develop, deploy, and maintain machine learning models in production environments.
The successful candidate will work on practical machine learning applications, including recommendation systems, personalised product recommendations, ranking algorithms, and search optimisation. The role involves translating business requirements into effective machine learning solutions while ensuring that models perform reliably at scale.
Working closely with data scientists, software engineers, and product stakeholders, you will help turn data-driven ideas into production-ready systems. You will also contribute to the development of MLOps pipelines that support automated model deployment, version control, monitoring, and continuous improvement.
Microsoft Azure will be a key part of the technology environment, with an emphasis on scalability, performance, and reliability. Candidates should have hands-on experience building and maintaining production machine learning systems, rather than focusing exclusively on experimentation or research.
Key Responsibilities
Machine Learning Model Development and Deployment
Design, develop, test, and deploy machine learning models into production environments.
Build practical ML solutions that address business requirements and improve product performance.
Optimise models to improve accuracy, efficiency, scalability, and reliability.
Work with relevant machine learning libraries and frameworks to develop maintainable solutions.
Ensure that models are suitable for integration into existing applications and technology platforms.
Recommendation Systems and Personalisation
Develop and optimise recommendation systems for personalised product recommendations, search, and ranking.
Implement collaborative filtering techniques to identify relevant recommendations based on user behaviour and preferences.
Apply content-based recommendation approaches using product attributes and other relevant information.
Develop hybrid recommendation models that combine multiple techniques where appropriate.
Evaluate recommendation quality and improve model performance using relevant data and feedback.
Collaborate with product stakeholders to align recommendation solutions with business objectives.
MLOps and Model Lifecycle Management
Implement and maintain MLOps pipelines to support continuous integration and continuous delivery of machine learning models.
Establish processes for model versioning, deployment automation, and performance monitoring.
Support repeatable and reliable model training, testing, and release workflows.
Monitor models after deployment to identify performance changes and potential issues.
Automate aspects of the machine learning lifecycle to improve efficiency and reduce manual intervention.
Contribute to best practices for model deployment, governance, and ongoing maintenance.
Azure Cloud Infrastructure
Develop and maintain machine learning solutions within Microsoft Azure cloud environments.
Help ensure that deployed models and supporting infrastructure meet performance, scalability, and reliability requirements.
Work with engineering teams to integrate ML systems into production technology environments.
Identify opportunities to optimise cloud-based machine learning workloads.
Support the effective operation and maintenance of machine learning services throughout their lifecycle.
Data Processing and Model Optimisation
Work with large-scale datasets to identify useful patterns, extract insights, and improve model accuracy.
Prepare and process data for model training, validation, and evaluation.
Collaborate with data scientists and engineers to improve data workflows and model inputs.
Assess model performance and identify opportunities for optimisation.
Retrain models when necessary to maintain their effectiveness as data, user behaviour, or business requirements change.
Cross-Functional Collaboration
Work with data scientists, software engineers, and product stakeholders to understand requirements and deliver ML solutions.
Translate business problems into practical machine learning approaches.
Communicate technical considerations, model performance, and implementation requirements clearly.
Contribute to engineering standards and best practices across model development, deployment, monitoring, and governance.
Required Skills And Experience
Candidates should have strong practical experience developing and operating machine learning systems in production environments.
The Key Requirements Include
Machine Learning Engineering: Demonstrable experience as a Machine Learning Engineer, particularly with production-grade ML solutions.
Microsoft Azure: Proven experience using Azure to support machine learning workloads and cloud-based deployments.
MLOps: Hands-on experience implementing MLOps practices, including CI/CD pipelines, model versioning, monitoring, and automation.
Recommendation Systems: Experience building recommendation engines using collaborative filtering, content-based methods, or hybrid approaches.
Python Programming: Strong proficiency in Python for machine learning development and data processing.
Machine Learning Frameworks: Experience with common ML libraries and frameworks, including TensorFlow and PyTorch.
Large-Scale Data Processing: Experience working with large datasets and the tools required to process and analyse them efficiently.
Model Monitoring and Maintenance: Understanding of model performance evaluation, retraining, and continuous improvement in production.
Applicants should be able to demonstrate how they have applied these skills to real-world machine learning systems.
Contract and Working Arrangements
This is a full-time, six-month contract based in North London, with hybrid working.
The role may suit an engineer who is comfortable working across model development, cloud infrastructure, recommendation systems, and production deployment. The initial contract duration is specified as six months; any possibility of extension, daily rate or salary, and the precise office attendance requirements should be confirmed with C4S Search Ltd.
How To Apply
Candidates interested in the Machine Learning Engineer opportunity should submit an application demonstrating their relevant technical experience and evidence of the required skills.
For further information, contact Shona at C4S Search Ltd. Applicants should highlight their experience with Azure, production machine learning, MLOps pipelines, recommendation systems, Python, and relevant ML frameworks.
Recruitment note: C4S Search Ltd. is the named recruitment company. The end employer, compensation, and exact hybrid working schedule are not specified in the supplied listing and should be confirmed with the recruiter.

Work arrangement
Yes