AI Engineer, Legal Innovation Team

Role Overview

• The AI Engineer is the technical builder within the Legal Innovation Team. The work is fundamentally applied natural language processing (NLP): extracting meaning from legal documents, classifying and comparing text, reasoning over complex material, and building systems that analyse language at scale.
• They take the specifications and requirements defined by Innovation Lawyers and turn them into working solutions: document processing pipelines, AI-powered analysis workflows and deployed systems that lawyers and clients use daily.
• This is not a generic software engineering role. The AI Engineer works exclusively on legal technology, embedded in a team that understands the domain deeply. They do not need to be a lawyer (that expertise sits with the Innovation Lawyers they work alongside) but they must build systems that meet the exacting standards legal work demands: accuracy, reliability, auditability, and data security.

Requirements

• Building document processing pipelines. Extracting text from PDFs, parsing structured and unstructured legal documents, transforming raw content into clean, queryable data. This is foundational work: every AI-powered solution starts with getting the data right.
• Designing and building NLP solutions for legal documents. Summarisation, classification, comparison, risk analysis, and information extraction, using large language models and other NLP techniques as appropriate. The output is always structured, auditable, and fit for use in legal work.
• Working with LLM APIs. Selecting appropriate models, designing effective prompts and processing chains, parsing structured outputs, implementing guardrails, and optimising for cost and performance. LLMs are the primary tool, but not the only one; the right approach depends on the task.
• Building web applications and user interfaces. Creating tools that lawyers and clients interact with directly, backed by databases, authentication, and audit logging.
• Designing evaluation frameworks. Building test datasets, defining quality benchmarks, and systematically measuring AI output accuracy rather than relying on spot-checking.
• Database design and data modelling. Structuring data stores that support both the deterministic extraction stages and the AI analysis stages of a pipeline, with clear separation of concerns and full auditability.
• Building internal tools that replace or improve upon expensive external services, with a focus on practical solutions that non-technical users can operate confidently.
• Integrating solutions with the firm’s technology stack, connecting AI capabilities with document management systems, Microsoft 365, and client-facing platforms.
• Ensuring security and compliance. All solutions must meet the firm’s data protection, confidentiality, and information security requirements. Legal data is among the most sensitive there is.
• Maintaining and iterating on deployed solutions based on user feedback and evolving requirements.

Benefits

• Competitive salary commensurate with experience, benchmarked against AI/ML engineering roles in the Dublin market.
• The firm offers a comprehensive benefits package.