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# Machine Learning Engineer

- **Company:** Anson McCade
- **Location:** London, UK
- **Experience:** Expert
- **Salary:** £70,000.0 - £190,000.0
- **Contract:** Permanent contract
- **Skills:** Amazon Web Services, Microsoft Azure, Python (Programming Language), Machine Learning, Tensorflow, Software Engineering, Pytorch, Build Management, Containerization, Scikit Learn, Kubernetes, Machine Learning Operations, Docker
- **Published:** September 12, 2026
- **Apply:** https://www.totaljobs.com/job/machine-learning-engineer/anson-mccade-job107972671

## About the Role

* Strong hands-on experience as a Machine Learning Engineer, ML Software Engineer or similar.
 * Strong Python skills and experience building production ML systems.
 * Experience taking machine learning models from development into production.
 * Practical experience with PyTorch, TensorFlow, Scikit-learn or similar ML frameworks.
 * Good understanding of core ML concepts, including statistics, probability and machine learning techniques.
 * Experience with cloud platforms such as AWS, Azure or GCP.
 * Experience with Docker and Kubernetes or similar containerisation/orchestration technologies.
 * Strong understanding of software engineering practices, system design and scalable architecture.
 * Ability to work closely with data scientists, engineers and non-technical stakeholders.
 * For Senior/Lead/Principal levels, we're looking for increasing levels of technical ownership, architectural decision-making and leadership.

## Description

You'll work alongside machine learning engineers, data scientists and technical teams to design, build and deploy scalable ML systems - with the autonomy to shape technical approaches and engineering best practice.
What you'll be doing

 * Build and deploy production-grade machine learning models, systems and infrastructure.
 * Take ML solutions through the full lifecycle, from experimentation and prototyping through to deployment, monitoring and iteration.
 * Work with frameworks such as PyTorch, TensorFlow and Scikit-learn.
 * Develop scalable ML pipelines, tooling and reusable components that accelerate the delivery of AI systems.
 * Make architectural and technical decisions around ML systems, infrastructure and deployment.
 * Work closely with data scientists and engineers to turn research and models into reliable production systems.
 * Apply strong software engineering practices to machine learning codebases and infrastructure.
 * Help define best practices for deploying and operating ML at scale.
 * Work directly with clients, translating complex ML concepts into practical technical solutions.
 * At senior levels, provide technical leadership and help shape ML engineering approaches across projects.


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