Leadership

• Design the operating model, team structure, delivery process and metrics for the Data Science & AI Engineering function.
• Hire, develop and lead a multidisciplinary team of data scientists, analysts and MLOps/AI engineers, setting strong technical and cultural standards.
• Own day-to-day operations — planning, prioritisation, delivery, performance management, mentoring and development.
• Build a collaborative, business-focused culture centred on practical AI and analytics outcomes.

Building & Running Internal AI Platforms

• Design, build and deploy internal AI applications on approved LLMs, with an eye on scale, evaluation, observability and cost.
• Lead development of the organisation’s RAG capability — ingestion, embeddings, vector/hybrid search, re-ranking and citation-backed responses across firm knowledge and matter data.
• Oversee integrations between AI tools and core business systems (document management, matter management, finance, timekeeping, knowledge management), using secure, governed frameworks such as MCP.
• Build AI-driven workflows and agent-based tools for legal/business processes, with proper governance, controls, traceability and human oversight.
• Direct model development, fine-tuning, evaluation and optimisation using proprietary data, in line with confidentiality, privilege, IP and security requirements.
• Lead analytics and reporting — data modelling, warehouse/lakehouse strategy, dashboards and BI tools giving leadership insight into operations, finance, staffing and AI usage.

MLOps, Engineering Excellence & Governance

• Set engineering and operational standards: source control, CI/CD, infrastructure-as-code, observability, environment management, evaluation and production support.
• Build and maintain MLOps/LLMOps capability: prompt/model versioning, automated testing, monitoring, drift detection, latency/cost tracking, incident management.
• Work with Information Security, IT, Privacy, Risk and General Counsel to ensure AI solutions meet confidentiality, privilege, client, data-governance and regulatory requirements.
• Support the organisation’s AI-governance framework — acceptable-use standards, vendor/model evaluation, testing and risk management.
• Assess build-vs-buy options, using commercial platforms where sensible and custom builds where they offer a strategic edge.

Cross-Firm Collaboration

• Align AI initiatives with Knowledge Management & Innovation priorities, business needs and adoption strategy.
• Partner with lawyers, practice groups and business stakeholders across the solution lifecycle.
• Communicate technical concepts and trade-offs clearly to business and legal audiences.
• Represent the organisation with vendors, clients, industry groups and the wider legal-AI community.