AI Engineer (x2) – Legal Innovation Team

Key Responsibilities

• 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 firms technology stack connecting AI capabilities with document management systems Microsoft 365 and client-facing platforms.
• Ensuring security and compliance. All solutions must meet the firms 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.

About the Person

• Strong proficiency in Python. The primary language our solutions are built in used for everything from data pipelines to web applications to LLM integration.
• Demonstrated experience with applied NLP. Text classification information extraction summarisation document comparison or similar tasks. NLP is the core discipline of this role; large language models are the primary tool but the engineer must understand the broader landscape of techniques and know when simpler approaches outperform generative models.
• Hands-on experience working with large language models. Prompt engineering API integration structured output parsing and systematic evaluation of model performance. We are looking for someone who has built real projects with LLMs not just experimented casually.
• A solid understanding of transformer architecture and how modern language models work. Not because we train models but because understanding the underlying mechanics leads to better system design more effective prompting and a realistic sense of what LLMs can and cannot do reliably.
• Experience with data processing and pipeline design. The ability to take messy real-world data (PDFs scanned documents inconsistent formats) and build robust pipelines that produce clean structured output. Comfort with SQL and data manipulation libraries is expected.
• Understanding of RAG architectures. Document chunking strategies embedding models vector databases retrieval strategies and the practical challenges of making retrieval-augmented generation work reliably on real-world documents.
• Familiarity with evaluation and benchmarking methodology. Designing test sets measuring AI system performance rigorously and building feedback loops that catch regressions. The nature of legal work means AI outputs must be held to a high standard and the engineer must be able to prove they meet it.
• Version control and collaborative development. Proficiency with Git and experience working in a shared codebase.
• A collaborative working style. This role is defined by working closely with non-technical colleagues (Innovation Lawyers). The ability to listen to requirements ask the right questions and translate domain knowledge into technical design is essential.

• A postgraduate qualification in computer science NLP machine learning or a related field or equivalent depth of expertise demonstrated through professional work and projects.
• Experience building document processing pipelines. PDF parsing OCR text extraction and structured data extraction from unstructured documents.
• Experience with a range of NLP techniques beyond LLM prompting such as classification pipelines similarity measures or heuristic-based extraction and the judgement to choose the right tool for the task.
• Exposure to agent frameworks and multi-step AI workflows (e.g. LangChain LangGraph Claude tool use OpenAI function calling).
• Experience with data visualisation and building dashboards or reporting interfaces that make complex data accessible to non-technical stakeholders.
• Familiarity with cloud platforms particularly Microsoft Azure given the firms infrastructure. Experience with Azure OpenAI Service or equivalent cloud AI services is a plus.
• A published research contribution (paper open-source project or technical blog) demonstrating depth of engagement with AI/ML topics.
• Experience communicating technical findings to non-technical audiences through reports presentations or stakeholder briefings.

• Solving real problems for real users. An engineer who cares about practical outcomes not just building technically impressive systems. The best solution is often the simplest one that works reliably.
• Comfortable working in an unfamiliar domain. Legal work is complex and nuanced and the right person is curious about it rather than intimidated by it.
• Balancing rigour with pace. Legal technology must be reliable enough to trust with client work but the speed of AI development means the team cannot afford to over-engineer every solution.
• Self-directed and resourceful. This is a small team building new things. There is no established playbook. The right person thrives in that environment.
• A natural communicator who can explain technical concepts clearly to lawyers and stakeholders who are not engineers.

• High-impact visible work. Every solution you build will be used by lawyers on real matters with real clients. The feedback loop is short and the impact is tangible.
• Cutting-edge technology. Legal AI is one of the most active applied AI domains. You will work with the latest models and techniques on genuinely challenging problems: long documents complex reasoning high accuracy requirements and real-world constraints.
• Domain depth over breadth. Rather than building generic AI applications you develop deep expertise in a specific intellectually rich domain. Legal reasoning contract analysis and regulatory compliance are hard problems that reward sustained focus.
• Autonomy and a small specialised team you own your solutions end-to-end from design through deployment. There is no handoff to another team.
• A stable well-resourced environment. Arthur Cox is one of Irelands leading law firms. This is not a startup that may not exist in 12 months. It is a long-established institution investing seriously in AI with the resources and client base to support ambitious work.

• The AI Engineer reports to the Senior Innovation Lawyer and works as part of the Legal Innovation Team.

Compensation & Benefits

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