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# Senior Machine Learning Engineer

- **Company:** Checkout.com
- **Location:** London, UK
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Cloud Computing, Monitoring of Systems, Python (Programming Language), Machine Learning, Raw Data, Cloud Services, Tensorflow, Software Deployment, Software Engineering, Pytorch, Apache Spark, Backend, Scikit Learn, Kubernetes, Xgboost, Build Tools, Machine Learning Operations, Data Pipelines, Databricks
- **Published:** July 11, 2026
- **Apply:** https://uk.indeed.com/viewjob?jk=519d7d14e96c30df

## About the Role

* 5+ years of experience as MLOps /ML Engineer
 * High proficiency in writing clear, production-ready Python code
 * Experience with production ML models (online or offline) and standard MLOps practices
 * Experience with monitoring and observability of production systems, with a strong sense of ownership
 * Experience with training and operating models on Databricks
 * Familiarity in Cloud-based application development (we use AWS & Azure)
 * Familiarity with one or more ML frameworks and technologies: scikit-learn, xgboost, TensorFlow, PyTorch, Spark, SageMaker, Vertex AI, Kubeflow, Seldon, Triton
 * Strong communication skills, able to express ideas clearly and collaborate across teams

## Description

* Build systems for training, deploying and monitoring machine learning models used in our Disputes platform, at scale
 * Build and optimize data pipelines and backend services to process dispute and payment data in real time
 * Build and scale our feature store for use-cases both online and offline
 * Take complete ownership of delivering comprehensive, end-to-end features within a startup-like setting, driving the entire lifecycle from requirement refinement, data pipeline construction and model training to troubleshooting and production deployment
 * Turn raw data into production-ready features that feed our dispute systems
 * Collaborate with platform and backend engineers to integrate models seamlessly


## Related Videos

- [Effective Machine Learning - Managing Complexity with MLOps](https://www.wearedevelopers.com/videos/185-effective-machine-learning-managing-complexity-with-mlops)
- [Developing the Backend with Stefan Lingler, CTO at Shpock](https://www.wearedevelopers.com/videos/100360-developing-the-backend-with-stefan-lingler-cto-at-shpock)
- [TikTok's Privacy Innovation](https://www.wearedevelopers.com/videos/1036-tiktok-s-privacy-innovation)
- [Photonic Computing: Programming a New Class of AI Accelerators (incl. Live Coding)](https://www.wearedevelopers.com/videos/100196-photonic-computing-programming-a-new-class-of-ai-accelerators-incl-live-coding)
- [DevOps for Machine Learning](https://www.wearedevelopers.com/videos/179-devops-for-machine-learning)
- [Nest.js - TypeScript in the backend can also be clean](https://www.wearedevelopers.com/videos/1033-nest-js-typescript-in-the-backend-can-also-be-clean)

## Related Articles

- [MLops – Deploying, Maintaining And Evolving Machine Learning Models in Production](https://www.wearedevelopers.com/magazine/115-mlops-deploying-maintaining-and-evolving-machine-learning-models-in-production)
- [MLOps – What’s the deal behind it?](https://www.wearedevelopers.com/magazine/125-mlops-what-s-the-deal-behind-it)
- [What Are Large Language Models?](https://www.wearedevelopers.com/magazine/304-what-are-large-language-models)
- [MLOps And AI Driven Development](https://www.wearedevelopers.com/magazine/82-mlops-and-ai-driven-development)
- [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer)
- [Dev Digest 137 - AI'm not sure about this](https://www.wearedevelopers.com/magazine/485-dev-digest-137-ai-m-not-sure-about-this)