Legal Knowledge Engineer

We are looking for a Legal Knowledge Engineer to help ensure we drive value from the systems that hold classify and surface our knowledge. You will use AI to enhance the firms knowledge and onboard teams to the firms knowledge practice this makes the role part builder part governor and part translator: building the classification and metadata that make content usable governing the quality of that work as it scales across teams and translating what fee earners need into changes the firms knowledge and AI systems can actually deliver.

This role requires a dual skillset. It calls for someone comfortable working hands-on with taxonomies metadata and AI tooling but equally comfortable working with lawyers to understand how they work and where knowledge is created lost or duplicated. The firm is consolidating its knowledge estate and connecting that knowledge to the tools lawyers use day to day - this role is central to making that happen.

The White and Case knowledge strategy rests on four pillars: clean data a single system governed AI assets and a culture of sharing. Our Legal Knowledge Engineer is the operational role that carries much of that work into individual practices. You will support the implementation of the firms strategic knowledge system partnering with PSLs/Knowledge Lawyers and supporting roles to drive value from our knowledge. A primary focus will be on automated classification at scale the classification playbook resourcing model and onboarding teams to the new systems.

Duties and Responsibilities

Classification Metadata and Enrichment (Pillar: Clean Up Data)

• Build classification capabilities for teams that need significant support taking a lead role in their design and working directly with lawyers to validate outputs before they are relied upon
• Provide a governance layer over classification work carried out by PSLs and other domain experts so that skills are built consistently produce good results and are measured properly rather than assumed to work.
• Design and run classification at scale so the firm can handle the large majority of tagging and profiling centrally with domain experts validating the output rather than manually re-profiling document by document.
• Use AI to enhance the firms knowledge: reducing the burden of long classification forms producing draft profiles for PSL review and enriching content with the metadata that makes it findable and useful.
• Define practice-specific metadata with each team including their legal topics document types and the data points that matter to how they practise.
• Bring rigour to how classification quality is assessed including the use of gold sets calibration and review feedback loops so confidence in a skills output is earned from evidence rather than asserted.

Requirements and Practice Engagement (Pillar: Single System)

• Work with fee earners and practice groups to understand their knowledge requirements across content features and data ensuring those needs inform the firms knowledge strategy and system development.
• Act as a point of contact for those who need help leveraging knowledge supporting them and building up their skills so the firms classification and curation capacity grows rather than staying concentrated in one place.
• Interface with Practice Solutions teams to identify where knowledge can be captured or consumed within the tools and workflows those teams own.

AI Assets and Downstream Systems (Pillar: Govern AI Assets)

• Govern the firms AI assets including prompts workflows and skills so they are portable and vendor-agnostic able to be shared and moved between systems rather than locked into a single platform.
• Work with downstream systems that consume knowledge to operationalise the connection of the firms knowledge into the tools where lawyers do their work.
• Help ground the output of AI tools in the firms approved knowledge so that what they produce reflects firm precedent rather than generic suggestions.

Onboarding Adoption and Culture (Pillar: Build Culture)

• Onboard teams onto the firms knowledge systems helping them get their content metadata and system configuration into good shape.
• Work with the Change Management team so that the communications and messaging around onboarding are clear and land with the people they are aimed at.
• Support efforts to instil a knowledge-sharing culture addressing not just whether people can share knowledge but whether they actually do. Part of this role is helping make knowledge contribution worthwhile and its value visible.
• Deploy strategies to capture relevant usage and content metrics to drive the firms knowledge strategy forward.
• Work with the firms search tooling at a tactical and feature level to make sure the firm is getting the most out of what it has bought.
• Look at how lawyers work in iManage and find the points where knowledge sharing and data capture can happen naturally as a by-product of normal work rather than a separate task.

Experimentation and Staying Ahead

• Run experiments on how the firm classifies know-how and final work at scale and turn what is learned into concrete proposals for what to try next.
• Maintain a working view of where knowledge tooling is heading including knowledge graphs retrieval-based approaches and the emerging patterns for structuring knowledge so it can be used by machines as well as people and judge what is worth bringing into the firms approach.