Senior MLOps Engineer
East Molesey, England, United Kingdom Full-Time
The Role:
We seek an experienced expert to contribute significantly to an R&D team, bridging machine learning engineering with applied data science. You will improve and manage Machine Learning Operations on Azure and participate in creating, assessing, and advancing various machine learning models and AI systems.
You will collaborate extensively with scientific and operational teams to ensure the robustness, scalability, and reliability of AI tools. By implementing automation and standardisation throughout the ML lifecycle, your work will support faster, data-informed decision-making and innovation.
Key Responsibilities:
Design, build, and maintain resilient Machine Learning Operations pipelines that support the complete lifecycle of AI models, from creation to implementation and supervision.
Ensure the successful deployment of machine learning and large language models in practical operational settings, transforming research findings into functional business tools.
Support the development and evaluation of ML models, including both standard machine learning and neural network-focused models, as requested by R&D teams.
Develop standardised, reusable workflows that can be applied across different projects and scientific areas.
Collaborate with scientists and engineers to incorporate AI solutions into daily R&D tasks.
Implement tools for version control, testing, and continuous integration to ensure quality, security, and traceability of AI solutions.
Develop automated reporting systems that make results from AI models easier to interpret, share, and act on.
Key Requirements:
MSc or BSc in Computer Science, Data Science, Bioinformatics, Engineering, or a related field, or equivalent experience.
Proven experience designing and deploying MLOps pipelines, including MLflow, Azure ML, Azure DevOps, or similar tools.
Strong programming skills in Python and familiarity with common ML/AI libraries, including Scikit-learn, TensorFlow, and Keras.
Experience implementing machine learning and large language models, including deployment, monitoring, and retraining.
Familiarity with software engineering guidelines, including version control, Git, CI/CD, containerisation, Docker, and workflow orchestration.
Knowledge of cloud platforms and scalable compute environments, with Azure preferred.
Understanding of data governance, model documentation, and reproducibility in a regulated or research-heavy context.
Ability to align machine learning initiatives with business objectives in a scientific or regulated environment.
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