Staff Software Engineer, AI Platform

JLL empowers you to shape a brighter way .   Our people at JLL are shaping the future of real estate for a better world by combining world class services, advisory and technology for our clients. We are committed to hiring the best, most talented people and empowering them to thrive, grow meaningful careers and to find a place where they belong.  Whether you’ve got deep experience in commercial real estate, skilled trades or technology, or you’re looking to apply your relevant experience to a new industry, join our team as we help shape a brighter way forward.

We are building JLL's internal AI platform: the layer every team in the firm uses to ship AI agents and AI-backed products. That means a gateway that puts a large and growing set of models from multiple providers behind one API, a shared chat surface that makes any agent reachable by employees across the firm, the developer tooling that takes an agent from an idea to something running in production, etc. You would join early enough to shape what this platform becomes. The foundations are live and already carrying production traffic, and the decisions still ahead of us are the ones that determine how every team at JLL builds with AI. Few engineers get the chance to set that direction at a firm this size, and fewer still get to see it in use across the business within weeks of building it.

This is a Staff level individual contributor role on a young platform. You will set technical direction rather than receive it: choosing the abstractions other teams will build against, deciding what belongs in the platform and what does not, and being accountable for whether those choices still look right a year from now. The work is genuinely both halves of the title. It is serious distributed systems engineering, with a gateway on the critical path of everything the firm builds, and it is AI engineering, where the hard problems are model routing, evaluation, guardrails, token cost and latency. We need someone who has shipped LLM backed systems in production and who would also be a strong platform engineer on any team.

Build the platform Own significant parts of the platform end to end, from the shape of the API through to how it behaves under load Be accountable for reliability and performance in production, on a gateway that sits on the critical path of everything the firm builds with AI Design the model gateway: provider abstraction, routing, failover, and making a provider switch a configuration change rather than a migration Make the agent surface work regardless of which framework a team chose or where their agent runs Solve the AI engineering problems Build the evaluation and benchmarking capability the platform needs, largely from scratch Design the guardrails that let an agent reach production safely, and help turn draft internal standards into something enforceable in code Own inference cost and latency as first class engineering concerns, including spend attribution and the routing decisions behind it Keep pace with a model landscape that changes weekly, and make onboarding a new model routine rather than a project Set the standard Make the compliant path the fast path, so that building on the platform is how a team clears architecture, security and responsible-AI review Be the engineer other teams bring their hardest integration problems to Raise the bar on observability before scale forces the issue Mentor across a distributed team, and leave behind designs and documentation that outlast your involvement What we're looking for Required 8+ years building and operating production systems Direct experience shipping agentic systems to production, not prototypes: agentic patterns, retrieval, tool calling, inference cost and latency, etc. Hands-on experience with more than one model provider, including dealing with the differences between them in practice Experience with evaluation, observability or guardrail