Artificial Intelligence Engineer

Do you want to have the chance to take ownership of a growing AI function and influence both the technical direction and long-term capability of a team?

You will join at a point where the foundations are still being shaped, which gives you space to design systems, introduce standards and lead key decisions.

You will also mentor junior engineers who are already in place, giving you a clear leadership path. The work involves varied challenges across research, applied engineering and production deployment, with an environment that supports autonomy and technical depth., The Senior AI Engineer will be responsible for designing, building and deploying AI systems, with a focus on text processing, NLP and LLMs. You will work across research and implementation, taking models from concept to production, setting technical standards and supporting junior engineers.

The role involves close collaboration with the AI, MLOps and Data Engineering teams, contributing to the wider technical strategy and ensuring models are reliable, maintainable and scalable., * Design and develop NLP and LLM-based systems for internal products and workflows.

• Evaluate new models, techniques and approaches, recommending what should be adopted.
• Fine-tune and adapt foundation models using domain-specific datasets.
• Carry out analysis to understand model behaviour, drift and explainability.
• Build and maintain tools for evaluation, prompt testing and dataset preparation.
• Work with the MLOps engineer to deploy, monitor and retrain models in production.
• Support CI/CD processes for AI, including version control, reproducibility and rollback workflows.
• Provide mentorship and guidance to junior engineers.
• Collaborate with product and engineering teams to integrate AI functionality into the platform.

• Experience as an AI or Machine Learning Engineer with responsibility for end-to-end model development and deployment.
• Strong knowledge of NLP and LLMs (transformers, fine-tuning, retrieval-augmented methods, agents).
• Experience conducting applied research and converting experimental outcomes into production systems.
• Familiarity with retraining workflows and performance monitoring.
• Understanding of explainability and fairness techniques.
• Hands-on experience with containerisation and orchestration (Docker/Kubernetes).
• Understanding of MLOps practices, CI/CD and model registry processes.
• Experience mentoring or guiding junior engineers or leading technical initiatives.

Nice to Have

• Experience working with sensitive or regulated data (not essential).
• Experience with graph-based retrieval.
• Familiarity with Azure ML or similar tooling.

Career Path and Growth

You will act as the senior figure within the AI group, shaping standards, guiding junior colleagues and influencing long-term technical decisions. Over time, this may evolve into broader leadership responsibilities as the team expands. The role suits someone who wants a balance of hands-on engineering, technical ownership and mentoring.