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# Senior Machine Learning Engineer

- **Company:** Llama Recruitment Solutions
- **Location:** Greater London, UK (Remote available)
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Amazon Web Services, Microsoft Azure, Cloud Computing, Continuous Integration, Data Governance, Linux, Python (Programming Language), Machine Learning, Tensorflow, Fast Healthcare Interoperability Resources, Kubernetes, Health Level Seven International, Machine Learning Operations, Data Pipelines, Docker
- **Published:** September 8, 2026
- **Apply:** https://www.collegerecruiter.com/job/2840621066-senior-machine-learning-engineer

## About the Role

* At least 3 years' experience building and deploying ML systems into production environments
 * Strong Python skills and hands-on experience with modern ML frameworks
 * Comfortable packaging, deploying, and supporting ML models using Docker and cloud infrastructure
 * Clear communicator who can work effectively across engineering, ML, and product teams
 * Proactive problem-solver who takes ownership in complex production settings
 * Background in healthcare and specifically medical imaging
 * Experience with cloud platforms such as AWS or Azure, including infrastructure as code
 * Familiarity with Kubernetes, Linux, CI/CD, data pipelines, and MLOps tooling
 * Exposure to health data standards such as HL7 or FHIR
 * Experience scaling ML systems or operating them under real-world constraints

## Description

This is a strong opportunity for a machine learning engineer ready to step into a senior, delivery-focused role, with clear ownership of models running in production and room to grow as the platform scales. You will work closely with ML scientists, engineers, and product leaders to ensure AI systems are robust, reliable, and deployable in a regulated healthcare environment., * Own the deployment, operation, and maintenance of machine learning models in production

 * Deliver high-quality, well-tested features that directly support early detection and treatment pathways
 * Collaborate with ML scientists to productionise models intended for clinical use
 * Design, maintain, and improve cloud infrastructure, CI/CD pipelines, and MLOps workflows
 * Monitor model performance and system health, troubleshooting and resolving production issues
 * Introduce pragmatic new technologies that improve reliability, scalability, or efficiency


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