Senior ML Engineer - Azure, MLOps & Recommendations

C4S Search Ltd; Greater London; Hybrid; GBP 70,000 - 100,000; Full time

Job summary

C4S Search Ltd in London is seeking a Machine Learning Engineer to design, build, and deploy ML models in production across Azure environments. You will develop and optimise recommender systems, collaborate with data scientists and engineers, and implement robust MLOps pipelines to ensure CI/CD, monitoring, and scalable deployments.

The role requires strong Python skills and hands-on experience with TensorFlow/PyTorch, plus familiarity with large-scale data processing.

Qualifications

• Production ML engineer with hands-on production experience.
• Knowledge of Azure and MLOps practices.
• Experience building recommendation systems (collaborative filtering, content-based, hybrid).
• Proficient in Python and ML libraries TensorFlow and PyTorch.

Responsibilities

• Design, build, and deploy ML models into production environments.
• Develop and optimise recommendation systems (personalised product recommendations, ranking, search optimisation).
• Collaborate with data scientists, engineers, and product stakeholders to translate business requirements into ML solutions.
• Implement and maintain MLOps pipelines for CI/CD, model versioning, monitoring, and automation.
• Ensure scalability, performance, and reliability of ML systems in Azure cloud infrastructure.
• Work with large-scale datasets to extract insights and improve model accuracy.
• Monitor model performance and retrain models to maintain effectiveness over time.
• Contribute to best practices in ML engineering, deployment, and governance.

Skills

Production ML experience

MLOps practices

Python programming

ML libraries: TensorFlow PyTorch

Large-scale data processing

Tools

Azure

CI/CD pipelines

Model versioning

Monitoring

Job description

C4S Search Ltd in London is seeking a Machine Learning Engineer to design, build, and deploy ML models in production across Azure environments. You will develop and optimise recommender systems, collaborate with data scientists and engineers, and implement robust MLOps pipelines to ensure CI/CD, monitoring, and scalable deployments.

The role requires strong Python skills and hands-on experience with TensorFlow/PyTorch, plus familiarity with large-scale data processing.