The AI Engineer will act as the technical lead within the Legal Innovation team, responsible for building and deploying AI-powered solutions focused on legal document analysis. Working closely with Innovation Lawyers, you will translate requirements into robust, scalable systems that meet the high standards required in a legal environment.

This is a hands-on role focused on applied NLP, large language models and end-to-end system development, rather than general software engineering.

Key Responsibilities

• Design and build document processing pipelines, transforming complex legal documents into structured, usable data.
• Develop NLP-driven solutions including summarisation, classification, comparison and information extraction.
• Work with large language models, including prompt design, API integration, output structuring and performance optimisation.
• Build and deploy user-facing tools and applications for internal and client use.
• Design evaluation frameworks to measure accuracy and ensure reliability of AI outputs.
• Develop data models and databases to support scalable and auditable AI workflows.
• Integrate AI solutions with internal systems, including document management and collaboration platforms.
• Ensure all solutions meet strict data security, confidentiality and compliance standards.
• Continuously refine and improve systems based on user feedback and evolving requirements.

This role will suit a technically strong AI Engineer with a focus on applied NLP and real-world problem solving. You will be comfortable working in a specialised domain and collaborating closely with non-technical stakeholders.

You will combine strong technical capability with a practical mindset, focusing on building reliable, usable solutions rather than purely theoretical models.

Skills & Experience

• Strong proficiency in Python, with experience building data pipelines and applications.
• Proven experience in applied NLP, including tasks such as classification, summarisation and information extraction.
• Hands-on experience working with large language models, including prompt engineering and API integration.
• Solid understanding of modern language model architectures and their practical applications.
• Experience designing and managing data pipelines, including handling unstructured data.
• Familiarity with retrieval-augmented generation (RAG) and related architectures.
• Experience with evaluation frameworks and performance benchmarking of AI systems.
• Strong version control experience (e.g. Git) and collaborative development practices.
• Ability to work closely with non-technical stakeholders and translate requirements into technical solutions.

Additional Experience (Desirable)

• Experience with document processing (PDF parsing, OCR, structured extraction).
• Exposure to advanced NLP techniques beyond LLMs.
• Experience with AI frameworks and multi-step workflows.
• Familiarity with cloud platforms, particularly Microsoft Azure.
• Experience building dashboards or visualisation tools.
• Evidence of deeper engagement in AI/ML (research, open-source or technical writing).