Product Engineer, Applied AI

Our Practice Solutions team leads the Firm’s innovation initiatives, supporting our lawyers in client delivery excellence. The team has deep expertise in working with established and emerging technologies, data analytics and data science, knowledge solutions and process re-engineering. The Practice Solutions team works with our lawyers to identify innovative solutions to support our clients. Through fostering a Firmwide culture of innovation, we encourage our lawyers to think creatively about how to deliver the best value to our clients and we ensure that White & Case remains at the forefront of advancements in the legal industry.Join White & Case’s Applied AI team to turn promising, rapidly developed prototypes into secure, reliable products that can be used and supported across the Firm. You will work directly with the Associate Director, Applied AI, to assess existing applications, decide what should be retained, refactored or rebuilt, and take ownership of the engineering needed to move them into production.

This includes integration, security, testing, deployment, monitoring and early live support, working closely with Technology and security colleagues. Our backend centres on Python, using FastAPI and FastMCP. Django is likely to be used for the first project, but the framework may vary for later projects depending on their needs. Many applications will be accessed through AI assistants via the Firm’s MCP gateway, making backend services, data modelling and secure integration central to the role. We need a practical product engineer who can bring structure to incomplete or experimental code, make sensible trade-offs and get useful software safely into users’ hands. Over time, there will also be opportunities to shape and build new prototypes.

Duties and Accountabilities:

• Assess prototypes for production readiness, including architecture, code quality, dependencies, data handling, security, scalability, supportability and user value. Recommend whether each prototype should be hardened, refactored, rebuilt or stopped, and agree a practical route to production.

• Build Python services, relational data models, APIs and background processing; add lightweight frontend interfaces where useful.

• Develop MCP integrations through the Firm’s gateway, handling authentication, permissions and token exchange with downstream applications.

• Work with Technology on approved cloud deployments and connections to on-premises systems, resolving infrastructure and integration dependencies.

• Follow the Firm’s central SDLC and security policies; implement testing, access controls, audit logging and secure secrets handling.

• Take production applications through deployment, controlled release and early live use, then establish clear ownership, support procedures, service expectations and handover arrangements with the relevant Technology teams.