Staff Software Engineer

JLL•Chicago, IL

2d•Onsite

About The Position

As a Staff Software Engineer on the MarTech Engineering Team, you'll serve as a forward-deployed engineer embedded within JLL's marketing organization, building production AI agents that solve real-world challenges across campaigns, content, social media, account-based marketing (ABM), and performance analytics. Working directly alongside marketing subject matter experts (SMEs), you'll immerse yourself in their workflows, learn their systems, and transform their expertise into reliable, production-grade AI agents that deliver measurable business value. Your work will span the complete agent lifecycle—from rapid prototyping and validation with SMEs, to building the integrations and capabilities that enable working pilots, to hardening those pilots into scalable, production-ready solutions that marketers trust and use daily. This role sits at the critical intersection of domain expertise and platform infrastructure, requiring deep technical judgment about when to build versus wait, when to abstract solutions into reusable components, and how to ensure agent outputs are safe, brand-aligned, and factually accurate across JLL's complex marketing technology stack. Success is measured not by demos or proof-of-concepts, but by the volume and quality of marketing work that agents are actually performing in production at one of the world's largest commercial real estate platforms.

Requirements

Bachelor's or Master's degree in Computer Science, Engineering, or a related field, or equivalent professional experience
5+ years of software engineering experience building and maintaining production systems, with significant experience integrating against enterprise APIs (CRMs, CMSes, DAMs, ad platforms, marketing automation tools, analytics platforms)
Hands-on experience with modern LLM APIs across multiple providers, including prompt engineering, tool use/function calling, structured outputs, and context engineering, with demonstrated understanding of transferable patterns across evolving APIs
Proven experience designing and implementing agent systems including multi-step reasoning, tool orchestration, memory management, error recovery, and human-in-the-loop escalation paths
Direct experience addressing production agent engineering challenges such as hallucination grounding, tool reliability and silent failures, evaluation design, cost and latency optimization, prompt drift, and the operational gap between demos and sustained production performance
Experience building RAG (Retrieval-Augmented Generation) systems including embeddings, vector stores, and retrieval optimization, with strong judgment about when retrieval versus alternative approaches (tool calls, fine-tuning, schema changes) is appropriate
Demonstrated ability to deploy LLM-powered services or agents to production cloud environments with understanding of authentication, networking, secrets management, observability, rollback procedures, and cost monitoring

Nice To Haves

Experience working directly with non-technical domain experts to translate ambiguous requirements into shippable technical scope
Track record of making pragmatic build-versus-wait decisions and knowing when to implement temporary workarounds versus building robust long-term solutions
Exceptional communication skills across technical levels—from explaining technical concepts to non-technical stakeholders, to presenting architectural tradeoffs to leadership, to deep technical discussions with engineering peers
Strong analytical and interpersonal skills with proven ability to thrive in dynamic, product-focused, distributed team environments
Proactive problem-solving approach with demonstrated willingness to acquire new skills and technologies as needed
Experience taking full ownership of projects from conception through production deployment
Contributions to engineering communities through blog posts, conference talks, open-source contributions, or technical publications

Responsibilities

Collaborate directly with marketing SMEs across campaigns, property marketing, content production, social media, ABM, and performance intelligence to design, prototype, and deploy AI agents that automate real marketing workflows
Build, deploy, and operate production marketing AI agents that reason over regional context, brand guidelines, and intent data to draft content, configure campaigns, monitor performance, and recommend optimizations
Design and expand the Marketing Domain Skills Library by extracting composable LLM workflows (drafting, scoring, classification, brand-voice tuning) from live agent implementations into reusable primitives
Develop integrations with marketing systems including CMS, DAM, CRM, marketing automation platforms, ad platforms, and analytics tools—building direct integrations to unblock pilots and coordinating with platform teams for long-term solutions
Own reliability, observability, evaluation, and cost efficiency of LLM-powered workflows in production, including implementing brand-voice checks, factual grounding mechanisms, regression test suites, and offline benchmarks integrated into CI/CD pipelines
Design multi-agent orchestration patterns that define how specialized agents (Campaigns, Social, ABM, Content, Property, Performance Intelligence) coordinate, where to compose versus maintain boundaries, and how escalations and handoffs flow
Represent MarTech Engineering to JLL leadership, customers, and the broader engineering community through internal documentation, engineering blog posts, conference presentations, and thought leadership on production AI agent systems