Enterprise AI Architect

Linklaters Linklaters is a global law firm, providing legal services in 20 countries and through 30 offices. Linklaters is a people business. Being best in class in the eyes of our clients means that our people must be exceptional. We look not only for brilliant minds, but for people who will thrive in our environment: people who love working collaboratively and demonstrate the innovative, efficient, agile, entrepreneurial, and responsible mind-set we aim to bring to every interaction. Ours is an environment of outperformance. We achieve this not with targets and incentives, but by fostering a positive, supportive, fair, and open atmosphere. We respect and value difference but insist on inclusivity. We celebrate all aspects of diversity and challenge any form of bias. This is vital to our ability to work as one team, with a common goal. Your Role The Enterprise AI Architect is a hands-on technical leader responsible for designing and evolving Linklaters' enterprise AI architecture (spanning generative AI, agentic systems, machine learning, and knowledge-driven platforms) to support the firm's ambition to be a world-leading, AI-powered law firm. Reporting to the Director of Data, the role sets the technical direction for how AI is built, integrated, governed, and scaled across the firm globally. The AI Architect partners with lawyers, business services, engineering, security, and risk teams to design solutions that are secure, explainable, cost-efficient, and aligned with the firm's responsible AI principles. Key responsibilities and challenges: • Working with the Director of Data, define and execute the firm's AI architecture vision, strategy, and roadmap, aligned with the firm's ambition to be a world-leading, AI-powered law firm. • Partner with the Director of Architecture, Enterprise Architects, Technology Strategy Lead, and Legal & Risk to shape, align, and secure agreement on the firm's AI strategy and architectural direction. • Design and maintain the firm's enterprise AI reference architecture, including patterns, standards, guardrails, and blueprints across generative AI, agentic AI, machine learning, and intelligent automation. • Architect agentic AI systems, including multi-agent orchestration, tool and function calling, human-in-the-loop, Model Context Protocol (MCP) patterns. • Set standards for LLM selection, routing, and orchestration across foundation models (e.g., Anthropic Claude, OpenAI, Google Gemini, open-source) and legal AI platforms (e.g., Harvey, Legora). • Define Retrieval-Augmented Generation (RAG) standards, embeddings, vector stores, hybrid retrieval, chunking, and re-ranking, tailored to legal content and knowledge. • Architect the firm's knowledge graph and context graph capabilities to ground AI in trusted firm data (matters, clients, precedents, know-how) and enable reliable agentic reasoning. • Establish context engineering standards, including prompt design, system prompts, skills/spec files, grounding, and context window optimisation. • Own the firm's AI platform blueprint, model/AI gateway, prompt management, evaluation harness, guardrails, observability, and feedback loops, and establish LLMOps and MLOps practices covering deployment, monitoring, drift, and lifecycle management. • Ensure AI solutions are scalable, reliable, secure, maintainable, and cost-efficient, and lead their integration across data platforms (Databricks), business systems, legal applications, and client-facing services. • Ensure compliance with the firm's data governance, model governance, information security, privacy, and confidentiality obligations, including client guidelines and legal considerations. • Partner with the Data & AI Design Authority, and Data Council to embed responsible AI principles, fairness, transparency, explainability, and human oversight, into architecture standards. • Design controls to mitigate AI-specific risks such as prompt injection, data leakage, hallucination and PII exposure, and ensure alignment with emerging regulation (e.g. EU AI Act, NIST, ISO/IEC 42001) • Establish AI FinOps patterns, token economics, model routing by cost and quality, caching, and chargeback/showback, to ensure AI investment delivers measurable value. • Act as the senior technical authority on AI architecture, providing guidance to engineering, data, business, and risk teams, and communicating complex AI concepts clearly to partner-level, C-suite, and non-technical audiences. • Provide technical leadership and mentoring to AI engineers, data scientists, and architects, fostering a culture of engineering excellence and responsible innovation. • Research and evaluate emerging AI technologies, models, and frameworks, translating them into reusable architecture patterns, responsible AI principles, and supporting platforms, tools, and frameworks for consistent adoption across the firm. Core Technical Skills & Experience: • 10+ years in enterprise architecture, with 3+ years designing and delivering production Generative AI and Agentic AI solutions at scale, ideally on Azure (preferred), AWS/ GCP using services such as Azure OpenAI or Vertex AI • Deep hands-on expertise with LLM architectures and foundation models (e.g. Anthropic Claude, OpenAI GPT, Google Gemini), including model selection, fine-tuning, distillation, and evaluation. • Proven experience designing RAG and agentic architectures, vector databases (Azure AI Search, Pinecone), embeddings, hybrid retrieval, multi-agent frameworks (LangGraph, AutoGen, CrewAI), MCP, and other orchestration patterns. • Strong understanding of knowledge and context graphs (Neo4j, Microsoft GraphRAG, RDF/OWL, ontology design), combined with hands-on LLMOps/MLOps tooling, Model Evaluation and prompt management • Strong grasp of AI security and Responsible AI, prompt injection defences, guardrails, and alignment with regulatory frameworks. About You • Extensive experience as a senior AI or enterprise architect, with a proven tra