Senior Software Engineer

JLL•Chicago, CO

22h•Onsite

About The Position

As a Staff Software Engineer on the MarTech Engineering Team, you'll be a forward deployed engineer embedded in the marketing organization, building production AI agents that solve real campaign, content, social, ABM, and performance problems. You'll work shoulder-to-shoulder with marketing domain experts (SMEs) — sitting in their workflows, learning their systems, and turning their expertise into agents that go live and stay live. Your remit spans the full lifecycle: rapid prototyping with SMEs to validate ideas, building the integrations and skills needed to ship a working pilot, and hardening pilots into production-grade agents that operate reliably at scale. When something is missing — a CRM that doesn't expose what you need, a data source not yet in the warehouse, a guardrail that doesn't exist — you'll either build a pragmatic workaround to unblock the pilot or translate the gap into a clear, prioritized requirement for our partner platform team — the engineers who own the underlying marketing systems, data plumbing, and AI-native tooling — to address. You sit at the seam between domain expertise and platform infrastructure, and your job is to make sure neither side waits on the other. The role demands deep technical judgment: when to build vs. wait, when to abstract a one-off solution into a reusable skill, and how to ensure agent outputs translate into safe, brand-aligned, and factually correct actions across a complex marketing stack. Success is measured by the volume and quality of marketing work that agents are actually doing in production — not by demos, not by POCs, but by agents that marketers trust to do the job. This is a fantastic opportunity to build the agentic layer of one of the world's largest commercial real estate platforms from the ground up, and to see your work change how marketing operates at global scale.

Requirements

Bachelor's or Master's degree in Computer Science, Engineering, or a related field, or equivalent work experience
Proficient in English, both written and verbal, sufficient for success in a remote and largely asynchronous work environment
5+ years of software engineering experience working in production systems, including significant time integrating against enterprise APIs (CRMs, CMSes, DAMs, ad platforms, marketing automation tools, analytics)
Hands-on experience with modern LLM APIs across providers — including prompt engineering, tool use / function calling, structured outputs, and context engineering — and you've worked with enough of them to know that the underlying patterns transfer even as the specific APIs evolve
Experience designing agent systems: multi-step reasoning, tool orchestration, memory, error recovery, and human-in-the-loop escalation paths
Worked through the hard parts of agent engineering firsthand — hallucination grounding, tool reliability and silent failures, evals that actually predict real-world behavior (and the gap when they don't), cost and latency tradeoffs, prompt drift, and the difference between "works in a demo" and "still works on day 30."
Experience building RAG systems — embeddings, vector stores, retrieval optimization, and grounding — and a strong intuition for when retrieval is the right answer vs. when a tool call, fine-tune, or schema change is the better lever
Deployed LLM-powered services or agents to production cloud environments and understand the operational reality — auth, networking, secrets, observability, rollback, cost monitoring — beyond the demo-day setup.
Energized by embedding directly with non-technical domain experts, can translate vague problem statements into shippable scope, and have the patience to learn an unfamiliar domain (marketing, in this case) deeply enough to anticipate where agents will fail
Pragmatic bias for shipping — you can tell when a brittle workaround is the right call to unblock a pilot and when something deserves to be built right the first time
Translate fluently between technical levels — from explaining agent failure modes to a non-technical marketing SME, to briefing leadership on architectural tradeoffs without dumbing them down, to going deep on protocol details with platform engineers, often within the same day.
Strong analytical and interpersonal skills, with a proven ability to thrive and collaborate in dynamic, product-focused, distributed teams
Embrace a proactive approach to problem-solving and a willingness to acquire new skills and knowledge as needed to achieve results
Confident taking ownership of projects from start to finish and enjoy turning nebulous ideas into reality
Make your coworkers feel included in every interaction

Responsibilities

Embed with marketing SMEs across campaigns, property marketing, content production, social, ABM, and performance intelligence to design and ship agents that do real work in their day-to-day
Build, deploy, and operate production marketing AI agents that reason over regional context, brand guidelines, and intent data — and invoke tools to draft content, configure campaigns, monitor performance, and recommend optimizations
Design and grow the Marketing Domain Skills Library — composable LLM workflows (drafting, scoring, classification, brand-voice tuning) extracted from live agent work as reusable primitives that multiple agents can call
Build integrations against marketing systems (CMS, DAM, CRM, marketing automation, ad platforms, analytics) — directly when needed to unblock a pilot, and through Marketing MCP servers built by that platform team once those exist
Translate integration and capability gaps you hit during pilots into clear, prioritized requirements for the platform team, so the platform layer evolves to meet real agent needs rather than speculative ones
Own reliability, observability, evaluation, and cost efficiency of LLM-powered workflows in production — including brand-voice checks, factual grounding against property and client data, regression suites, and offline benchmarks wired into CI/CD
Design multi-agent orchestration patterns: how the Campaigns agent coordinates with Social, ABM, Content, Property, and Performance Intelligence; where to compose vs. where to keep boundaries; how escalations and handoffs flow
Set the bar for the agent pod: define the playbook for going from SME conversation to working pilot to deployed prod agent, and raise the technical quality of what the team ships
Represent MarTech Engineering externally — to JLL leadership, to customers, and in the broader engineering community — as a credible voice on building agents that actually work in production
Publish what you learn — internal write-ups, engineering blog posts, and conference talks. Forward deployed agent work surfaces novel problems and solutions every week, and externalizing them sharpens the team's thinking, raises our hiring bar, and contributes back to the broader agent engineering community
Stay at the frontier of agent engineering and bring the best ideas back to the team, continuously raising the bar on quality, performance, and architecture at scale
Drive innovation with a willingness to experiment and to boldly confront problems of immense complexity and scope