About the teamWe 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.
About the roleThis 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.
What you'll doBuild 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 forRequired- 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 tooling for agentic systems
- Strong platform engineering track record, on systems where other engineering teams were your users
- Hands-on depth in at least one major public cloud, including running and debugging production workloads
- Experience designing APIs and abstractions that others build against and that you then have to keep stable
- Demonstrable production ownership: on-call, incident response, root cause analysis
- A track record of pushing work through a large organisation: navigating process, winning the argument, and getting decisions unblocked rather than waiting on them
- Excellent English communication, written and spoken
Preferred- Built or operated an internal developer platform or shared multi-tenant service
- Kubernetes and container-based delivery
- Infrastructure as code
- Enterprise security, data privacy or responsible-AI review processes
How we work: - A small team that moves quickly inside a very large organisation, where the fastest route is rarely the obvious one
- An early stage platform where the roadmap is still being written
- Deciding without complete information, and revisiting the decision when it turns out to be wrong
- A distributed team with limited timezone overlap
- Production users from day one, on a platform still being built underneath them
This position does not provide visa sponsorship. Candidates must be authorized to work in the United States without sponsorship.
Estimated compensation for this position:160,000.00 - 260,000.00 USD per year
This range is an estimate and actual compensation may differ. Final compensation packages are determined by various considerations including but not limited to candidate qualifications, location, market conditions, and internal considerations.
Location:Remote -Chicago, IL
If this job description resonates with you, we encourage you to apply, even if you don't meet all the requirements. We're interested in getting to know you and what you bring to the table!
Personalized benefits that support personal well-being and growth:JLL recognizes the impact that the workplace can have on your wellness, so we offer a supportive culture and comprehensive benefits package that prioritizes mental, physical and emotional health. Some of these benefits may include:
- 401(k) plan with matching company contributions
- Comprehensive Medical, Dental & Vision Care
- Paid parental leave at 100% of salary
- Paid Time Off and Company Holidays
- Early access to earned wages through Daily Pay
