Machine Learning Engineer developing and implementing ML models for lending at Blue Whale Lending LLC. Collaborating with teams to enhance data insights and validate model performance.
About the role
- Senior ML Engineer at BMT designing and deploying machine-learning systems, collaborating across diverse client projects in defence and security sectors.
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, refines 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 participates 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 advises 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.
Requirements
- 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.
- 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.
- Experience containerising and deploying models (e.g., Docker), implement CI/CD, monitoring, drift detection, and automated retraining on Azure/AWS/GCP as appropriate.
- Demonstrated capability to work with data engineers to ensure high‑quality datasets, versioning, lineage, and governance; champion data quality checks and observability.
- Capable of pairing with data scientists and software engineers, review code, and share best practices; coach juniors and foster a culture of continual improvement.
- Experience with evaluating emerging techniques, creating reusable components/templates, and feeding learning back into internal libraries and delivery playbooks.
- Strong engineering skills in Python (typing, testing, packaging); experience with version control (Git) and code review workflows.
- Hands‑on experience building and shipping ML models; solid understanding of metrics, validation strategies, and responsible AI considerations.
- 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.
- Proficiency with MLOps platforms and workflow tools such as MLflow, Airflow, Kubeflow, SageMaker, or Azure ML.
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
Job title
Job type
Experience level
Salary
Not specified
Degree requirement
No Education Requirement
Tech skills
Location requirements
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