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# Machine Learning - Engineer - London

- **Company:** Michael Page
- **Location:** London, UK
- **Salary:** £75,000.0 - £95,000.0
- **Contract:** Permanent contract
- **Skills:** Application Programming Interfaces (APIs), Microsoft Azure, Cloud Computing, Continuous Integration, Data Cleansing, Python (Programming Language), Machine Learning, Tensorflow, Pytorch, Large Language Models, Deep Learning, HuggingFace, Machine Learning Operations, Software Version Control, Docker
- **Published:** August 19, 2026
- **Apply:** https://find.jobs/jobs-near-me/apply/ats-redirect/?id=2933562768-2

## About the Role

Successful ApplicantMachine Learning Engineer - LondonA successful Machine Learning Engineer should have:Strong foundation in machine learning, deep learning, and optimisationHands-on experience training, evaluating, and deploying ML models in real-world systemsProficiency with PyTorch (preferred) or TensorFlow; familiarity with the Hugging Face ecosystemExperience with audio or speech models and frameworks.Experience building and maintaining end-to-end ML pipelines and MLOps tooling (e.g. MLflow, Weights & Biases, DVC, or similar).Strong Python skills; experience with Docker, CI/CD, and cloud platforms (Azure preferred)Practical experience designing agentic AI systems and integrating models with external servicesComfortable owning the full ML lifecycle, from data preparation to production deploymentClear communicator who can work effectively across technical and non-technical teamsWhat's on OfferMachine Learning Engineer - LondonCompetitive salary ranging from 75,000 to 95,000 per

## Description

(ASR), and voice activity detection.Build and maintain training and inference pipelines using PyTorch, and related ML frameworksSource, curate, and prepare training datasets; implement preprocessing, augmentation, and validation workflows.Run structured experiments, evaluate model performance, and iterate based on measurable resultsBuild, deploy, and operate end-to-end MLOps pipelines, including experiment tracking, model versioning, and production monitoring.Package and deploy models using Docker and cloud infrastructure, with a focus on reliability and scalabilityDesign and deploy agent-based AI systems that can execute multi-step workflows and integrate with external tools.Build Model Context Protocol (MCP) servers to enable standardised integration between models, APIs, and data sources.Evaluate and integrate large language models into production systems where they add clear value.Collaborate with product and business teams to translate requirements into practical ML solutions.The


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