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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Senior ML Engineer - **Company:** BMT - **Location:** Bath, UK - **Experience:** Expert - **Contract:** Permanent contract - **Skills:** Application Programming Interfaces (APIs), Airflow, Amazon Web Services, Microsoft Azure, Code Review, Continuous Delivery, Continuous Integration, Data Integrity, Data Structures, Monitoring of Systems, Python (Programming Language), Machine Learning, Metadata Standards, Tensorflow, DataOps, Azure Machine Learning, Strategies of Testing, Management of Software Versions, Feature Engineering, Data Ingestion, Pytorch, Large Language Models, Deep Learning, Git, SC Clearance, Containerization, Scikit Learn, Kubernetes, Data Lineage, Machine Learning Operations, Feature Extraction, Data Pipelines, Docker - **Published:** September 10, 2026 - **Apply:** https://www.collegerecruiter.com/job/2840589675-senior-ml-engineer ## About the Role * Be a UK sole national. * Have held no other nationality at any time. * Have continuously resided in the United Kingdom for the past five years. * Be able to obtain and maintain full UK security clearance in accordance with government vetting standards. * Provide satisfactory evidence of identity, nationality, and residency as part of the clearance process., As the Senior ML Engineer, you will have skills in: * Solution Engineering: Capability to design and implement end-to-end ML pipelines (data ingestion * feature engineering * training * evaluation * deployment). * Model Development: Ability to select, train, and tune models (classical ML and deep learning) using frameworks such as PyTorch, TensorFlow, or scikit-learn. * MLOps & Productionisation: Experience containerising and deploying models (e.g., Docker), implementing CI/CD, monitoring, drift detection, and automated retraining on Azure/AWS/GCP. * DataOps & Quality: Demonstrated capability to work with data engineers for high-quality datasets, versioning, lineage, and governance. * Collaboration & Mentoring: Pairing with data scientists and software engineers, reviewing code, sharing best practices, coaching juniors. * Research & Reuse: Evaluating emerging techniques, creating reusable components/templates. * Software Foundations: Strong engineering skills in Python (typing, testing, packaging), experience with Git and code review workflows. * ML Expertise: Hands-on experience building and shipping ML models; solid understanding of metrics, validation strategies, and responsible AI considerations. * MLOps Engineering: Experience with cloud ML platforms (Azure Machine Learning or AWS/GCP equivalents), CI/CD tooling, containerisation using Docker, model monitoring. * MLOps Frameworks: Proficiency with MLflow, Airflow, Kubeflow, SageMaker, or Azure ML. Missing skills? Let us be the judge! BMT are passionate about people; we recognise that technology moves quickly and that no one can learn everything, which is why we seek those who can adapt and demonstrate the aptitude to learn. With enthusiasm and the right attitude, we can help you discover your potential. ## Description We are happy to explore flexible and hybrid working arrangements. Please note that travel to customer sites or to attend meetings will be required., We are seeking an experienced Senior ML to join our team and engage in a diverse range of client projects within the defence, national security, and commercial sectors. At BMT we accelerate our business through informed and targeted application of ML and LLMs. As a Senior ML Engineer, you will be responsible for: * Design, build, and deployment of machine-learning systems, applying robust software engineering practices and an in-depth understanding of model behaviour, performance, and limitations. * Select, prepare, and pipeline data for model training and inference. Implements, trains, evaluates, and optimises machine-learning models, continually improving them through iterative experimentation and additional data. * Create scalable and automated ML pipelines, including feature extraction, model training, validation, packaging, deployment, and monitoring. * Design and implement dashboards, diagnostics, and evaluation tooling to ensure transparency, performance tracking, and operational reliability across the ML lifecycle. * Within defined delivery goals, refine prototype models into production-ready components, contributing to development, optimisation, demonstration, and integration activities. * Apply standardized engineering and evaluation methods, producing clear technical documentation and communicating design choices, performance outcomes, and limitations. * Contribute to internal knowledge bases and participate in professional ML engineering communities. * Ensure responsible handling of data throughout the ML lifecycle, including secure storage, access control, data lineage, versioning, and quality checks. * Evaluate data integrity and suitability for ML workflows, and advise on transformations, feature representation, and schemas needed for efficient training and inference. * Implement metadata standards, reproducible data pipelines, and automated validation procedures to maintain trustworthy data assets. * Design, develop, test, document, and maintain moderately complex machine-learning services, APIs, and supporting software. * Write well-structured, maintainable code using agreed standards and tools. * Apply engineering-focused data modelling and system design techniques to create, modify, or maintain ML-relevant data structures, feature stores, and associated components. ## Related Videos - [Effective Machine Learning - Managing Complexity with MLOps](https://www.wearedevelopers.com/videos/185-effective-machine-learning-managing-complexity-with-mlops) - [How a Small Team Shrank a Microsoft Monorepo by 94%](https://www.wearedevelopers.com/videos/1236-how-a-small-team-shrank-a-microsoft-monorepo-by-94) - [Docker Compose: Rediscovered](https://www.wearedevelopers.com/videos/1978-docker-compose-rediscovered) - [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) - [Serverless deployment of (large) NLP models ](https://www.wearedevelopers.com/videos/158-serverless-deployment-of-large-nlp-models) - [Introduction to Azure Machine Learning](https://www.wearedevelopers.com/videos/368-introduction-to-azure-machine-learning) ## 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) - [What Are Large Language Models?](https://www.wearedevelopers.com/magazine/304-what-are-large-language-models) - [Data Engineer Salary UK](https://www.wearedevelopers.com/magazine/253-data-engineer-salary-uk) - [Why Upskilling And Reskilling is Important For Developers](https://www.wearedevelopers.com/magazine/428-why-upskilling-and-reskilling-is-important-for-developers) - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [Software Engineer Salary London](https://www.wearedevelopers.com/magazine/252-software-engineer-salary-london)