Senior Software Engineer

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.   

As 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.

We'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

• Design 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

• Experience 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

Location:

Scheduled Weekly Hours:

If this job description resonates with you, we encourage you to apply even if you don’t meet all of the requirements.  We’re interested in getting to know you and what you bring to the table!

At JLL, we harness the power of artificial intelligence (AI) to efficiently accelerate meaningful connections between candidates and opportunities. Using AI capabilities, we analyze your application for relevant skills, experiences, and qualifications to generate valuable insights about how your unique profile aligns with the specific requirements of the role you're pursuing.

JLL Privacy Notice

Jones Lang LaSalle (JLL), together with its subsidiaries and affiliates, is a leading global provider of real estate and investment management services. We take our responsibility to protect the personal information provided to us seriously. Generally the personal information we collect from you are for the purposes of processing in connection with JLL’s recruitment process. We endeavour to keep your personal information secure with appropriate level of security and keep for as long as we need it for legitimate business or legal reasons. We will then delete it safely and securely.

For more information about how JLL processes your personal data, please view our Candidate Privacy Statement.

For additional details please see our career site pages for each country.

Jones Lang LaSalle (“JLL”) is an Equal Opportunity Employer and is committed to working with and providing reasonable accommodations to individuals with disabilities.  If you need a reasonable accommodation because of a disability for any part of the employment process – including the online application and/or overall selection process – you may email us at [email protected]. This email is only to request an accommodation. Please direct any other general recruiting inquiries to our Contact Us page > I want to work for JLL.