JLL supports the Whole You, personally and professionally.
Our people at JLL are shaping the future of real estate for a better world by combining world class services, advisory and technology to our clients. We are committed to hiring the best, most talented people in our industry; and we support them through professional growth, flexibility, and personalized benefits to manage life in and outside of work. Whether you’ve got deep experience in commercial real estate, skilled trades, and technology, or you’re looking to apply your relevant experience to a new industry, we empower you to shape a brighter way forward so you can thrive professionally and personally.
About the RoleAs a Senior Full Stack Software Engineer on the Intelligence Pod, you'll build and operate the integrations, data infrastructure, and MCPs (Model Context Protocol servers) that power AI agents across the marketing organization. You're not building features for marketers to interact with directly — you're building the plumbing that agents depend on: stable, performant access to enterprise marketing systems (Adobe Experience Manager, DAM, contact management, event management, analytics), with the right data governance, caching, and error handling baked in.
Your work spans building new MCPs against marketing systems, designing data access patterns that agents can rely on, maintaining and hardening integrations as they scale from pilot to production volume, and identifying opportunities to abstract patterns into reusable capabilities that multiple agents can safely consume. You'll work closely with senior engineers and the Agent Pod (who consumes your integrations) to understand what agents need, validate your designs against real agent workflows, and iterate toward stable, eval-ready capability surfaces.
The role demands solid technical judgment: understanding API design from the agent perspective (what does an agent actually need to do its job?), how to handle the messy reality of enterprise systems (rate limits, inconsistent auth, data quality issues), and how to build infrastructure that stays reliable when demand changes. Success is measured by the stability and scale of integrations you ship, the reliability of data flowing through them, and how confidently agents can depend on what you build.
Who You AreWe're optimizing for capability, curiosity, and collaboration over a specific tech stack. The tools change; the way you think and work with others doesn't.
You have a Bachelor's degree in Computer Science, Engineering, or a related field, or equivalent hands-on work experience
You are proficient in English, both written and verbal, sufficient for success in a remote and largely asynchronous work environment
You have 5+ years of software engineering experience building production systems
You're comfortable building backend systems and data infrastructure: you can design and implement REST/GraphQL APIs, understand how to connect systems together reliably, and know how to deploy and monitor services in cloud environments (AWS, GCP, or Azure)
You have hands-on experience with C# or Java as your primary backend runtime and understand how to build services that handle high throughput, scale, and reliability
You've integrated against external APIs in production — you understand OAuth/API key auth, rate limiting, pagination, error handling, retries, and the pain points of building against systems you don't control
You have experience building and consuming data APIs or working with structured data schemas; you think clearly about data flow and consistency
You're comfortable with relational databases (PostgreSQL, MySQL) and understand query performance and basic optimization; bonus points if you've worked with document stores or message queues
You've worked on codebases with other engineers and understand the value of code review, testing, and incremental refactoring
You can debug systematically — read logs, trace requests through systems, form hypotheses, and validate them — and you're not afraid to dive into unfamiliar code to understand what's happening
You're genuinely curious about how agents will consume the integrations and data you build — you ask questions to understand agent workflows, edge cases, and failure modes before they become production incidents
You communicate clearly in writing — you can explain technical decisions in PRs, document tradeoffs, and ask clarifying questions without leaving teammates guessing
You have a pragmatic bias toward shipping and iterating: you can tell the difference between a quick fix that unblocks progress and something that needs to be built right
You're energized by learning how complex enterprise systems (Adobe AEM, DAM platforms, marketing automation tools) work and translating their capabilities into clean, reliable APIs that agents can depend on
You're proactive, reliable, and take ownership of your work — you follow up on issues, communicate when things are blocked, and pitch in on the unglamorous work that needs to happen
You make your coworkers feel included and genuinely want to lift the team's bar
What You'll DoDesign and build MCPs (Model Context Protocol servers) that expose marketing system data (AEM, DAM, contact management, event management) as stable, agent-consumable APIs
Implement integrations against complex enterprise marketing platforms — handling authentication schemes, pagination, rate limiting, data transformation, and the edge cases that always hide until production
Own reliability and observability: design MCPs with proper error handling, timeout strategies, caching, monitoring, and alerting so agents can depend on them
Work directly with the Agent Pod to understand what integration patterns agents need, validate your API designs against real agent workflows, and iterate based on what actually works
Build data infrastructure that powers agents: designing schemas, data models, and consistency guarantees that agents can rely on; understanding when to denormalize for performance and when to maintain data integrity
Handle the hard infrastructure problems: pagination across multiple API calls, staleness/freshness tradeoffs, partial failure recovery, circuit breakers and fallbacks when upstream systems go down
Participate in architecture design with your peers and leads — how should AEM integrations differ from DAM integrations? When should we build shared patterns vs. system-specific optimizations?
Write tests that matter: integration tests against real system APIs (or close simulations), regression tests for failure modes, and evals that predict real-world behavior before agents see it
Debug production issues when they arise: understand why agents got stale data, why an integration started timing out, why a third-party API changed and broke expectations
Help the team understand the data landscape: what data exists where, what guarantees different systems provide, where data consistency breaks, what needs fixing upstream
Collaborate with the platform team to translate integration gaps into clear, prioritized requirements for the underlying marketing systems infrastructure
Own the operational reality: deployment, rollback, secrets management, monitoring and alerting for integrations you ship
Learn the marketing tech stack deeply: understand how AEM, DAM, contact management, and other marketing systems work, their limitations, and where agents will run into walls
Continuously improve: refactor integrations, extract reusable patterns into shared libraries, reduce technical debt, and raise the bar for what "production-ready" means
Nice to HaveExperience building MCPs or similar agentic infrastructure
Experience with LLM APIs or AI-powered applications (understanding what agents actually need from integrations)
Familiarity with marketing systems or martech platforms (Adobe AEM, Adobe DAM, Salesforce, HubSpot, etc.)
Experience deploying and monitoring services in production (Docker, Kubernetes, CI/CD pipelines, observability stacks)
Exposure to API design patterns and thinking about APIs from a consumer (agent) perspective
Exposure to data engineering or analytics — building pipelines, understanding data quality, working with data warehouses
Experience with event-driven architecture or message queues
Open source contributions or public projects you can point to
If this job description resonates with you, we encourage you to apply even if you don’t meet all of the requirements below. 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.
About JLL –
We’re JLL—a leading professional services and investment management firm specializing in real estate. We have operations in over 80 countries and a workforce of over 102,000 individuals around the world who help real estate owners, occupiers and investors achieve their business ambitions. As a global Fortune 500 company, we also have an inherent responsibility to drive sustainability and corporate social responsibility. That’s why we’re committed to our purpose to shape the future of real estate for a better world. We’re using the most advanced technology to create rewarding opportunities, amazing spaces and sustainable real estate solutions for our clients, our people, and our communities.
Our core values of teamwork, ethics and excellence are also fundamental to everything we do and we’re honored to be recognized with awards for our success by organizations both globally and locally.
Creating a diverse and inclusive culture where we all feel welcomed, valued and empowered to achieve our full potential is important to who we are today and where we’re headed in the future. And we know that unique backgrounds, experiences and perspectives help us think bigger, spark innovation and succeed together.
