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 are looking to accelerate all of our business through informed and targeted application of ML and LLMs. As a Senior ML Engineer, you will be responsible for:
Responsibilities
* 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 standardised 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. Supports alignment of data structures, model interfaces, and infrastructure components to ensure efficient and scalable ML system operation.
Key Skills
* Solution Engineering: Capability to design and implement end‑to‑end ML pipelines (data ingestion, feature engineering, training, evaluation, deployment), favouring scalable, reproducible, testable code and strong software practices.
* Model Development: Ability to select, train, and tune models (classical ML and deep learning) using frameworks such as PyTorch, TensorFlow, or scikit‑learn; perform robust validation and error analysis.
* MLOps & Productionisation: Experience containerising and deploying models (e.g., Docker), implementing CI/CD, monitoring, drift detection, and automated retraining on Azure/AWS/GCP as appropriate.
* DataOps & Quality: Demonstrated capability to work with data engineers to ensure high‑quality datasets, versioning, lineage, and governance; champion data quality checks and observability.
* Collaboration & Mentoring: Capable of pairing with data scientists and software engineers, reviewing code, and sharing best practices; coach juniors and foster a culture of continual improvement.
* Research & Reuse: Experience with evaluating emerging techniques, creating reusable components/templates, and feeding learning back into internal libraries and delivery playbooks.
* Software Foundations: Strong engineering skills in Python (typing, testing, packaging); experience with version control (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 (GitHub Actions, Azure DevOps), containerisation using Docker, and implementing model monitoring in production environments.
* MLOps Frameworks: Proficiency with MLOps platforms and workflow tools such as 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.
BMT is dedicated to tackling the most crucial engineering challenges of our era, fostering an environment where individuals with exceptional technical expertise provide meaningful, practical solutions. Committed to creating a safer, more efficient, effective, and sustainable future, BMT values diversity, equity, and inclusion, recognising their pivotal role in achieving our business purpose. Learn more about BMT at www.bmt.org. At BMT, we pride ourselves on being a verified flexible workplace, providing freedom and choice. We understand not everyone has the same needs in order to make work work for them and their lives – we'd love you to discuss any requirements for flexibility with us – we can't promise to fulfil every request but we do promise to listen to what matters to you. Learn more about our flexible status at Flexa Careers.
Benefits
* Private Medical (family coverage)
* Enhanced Pension
* 18 weeks enhanced maternity pay (after a qualifying period of 1 year)
* Family friendly policies
* Committed to an inclusive culture
* Wellbeing Fund – an annual fund for personal hobbies or interests
* 26 Days Annual Leave (plus bank holidays)
* Holiday Trading
* Retail Vouchers
* Professional Subscriptions
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