Machine Learning Engineer

This position combines hands-on machine learning engineering with the opportunity to contribute to the development and direction of AI initiatives. You will work on solutions that move beyond experimentation and into production environments. The Role As a Senior AI/ML Engineer, you will work across the full machine-learning lifecycle, including experimentation, model development, deployment, and production monitoring. The position has a strong focus on Natural Language Processing (NLP) and applying modern machine-learning techniques to complex, real-world datasets. You will work closely with technical, product, and domain specialists to develop reliable AI systems and help establish scalable ML engineering practices. Responsibilities

• Design, train, evaluate, and deploy machine-learning models with a particular focus on NLP and text-based applications.
• Build and maintain end-to-end ML pipelines covering data preparation, training, evaluation, deployment, and monitoring.
• Work with modern NLP approaches including Transformers, LLMs, and encoder/decoder architectures.
• Develop and improve MLOps processes, including experiment tracking, model versioning, automated training, and deployment workflows.
• Optimise models for production environments, considering performance, latency, scalability, and resource efficiency.
• Work with complex datasets and develop approaches for data preparation, quality, anonymisation, and standardisation.
• Contribute to technical and research initiatives and help shape the organizations broader AI capabilities.
• Collaborate with engineers and domain experts to move ML solutions from initial concepts into production., The technology environment includes tools and technologies across: Machine Learning: Python, PyTorch/TensorFlow, Transformers, NLP libraries MLOps: Experiment tracking, Docker, Kubernetes, CI/CD Infrastructure: Cloud infrastructure, relational databases, container orchestration, monitoring Compute: GPU-based training and inference environments The Opportunity This is an opportunity to work on production AI systems with meaningful real-world applications, while having significant ownership over how machine-learning solutions are designed, developed, deployed, and improved. You will join an experienced technical team where you can contribute both to hands-on engineering and the longer-term development of the organisations AI capabilities. Apply

• 3+ years of hands-on professional experience within Machine Learning or Artificial Intelligence, ideally with a strong focus on NLP.
• Strong Python development skills.
• Commercial experience with a major ML framework such as PyTorch or TensorFlow.
• Experience working with the HuggingFace ecosystem or similar NLP tooling.
• Strong understanding of modern NLP techniques including Transformers and LLMs.
• Experience deploying machine-learning models into production environments.
• Understanding of MLOps practices and technologies such as Docker, CI/CD, experiment tracking, and model monitoring.
• Strong software-engineering fundamentals, including Git, testing, code reviews, and documentation.
• Ability to independently take technical projects from initial concept through to production.

Desirable Experience Experience in any of the following would be advantageous:

• Working with healthcare, medical, scientific, or similarly complex datasets.
• Building AI solutions in regulated or data-sensitive environments.
• Kubernetes or cloud-based ML infrastructure.
• Research or academic experience within ML/NLP.
• Publications or contributions to ML/NLP research.
• Master’s degree or PhD in Computer Science, Machine Learning, Computational Linguistics, or a related discipline.