Fifthwall Analytics is building the operating layer for AI-powered real estate businesses. We partner with real estate companies to identify high-value operational pain points, then design and deploy tailored AI solutions that drive measurable outcomes. Overview As our Forward Deployed Engineer, you will be the person who makes it real. You will sit at the intersection of AI engineering, client delivery, and CRE domain translation taking a brief, determining the technical approach independently, and shipping AI solutions that clients actually use. This is not a role for someone who wants to work in isolation. You will be embedded in client environments, building production-grade AI workflows while the Engagement Manager runs the relationship with you. Role Details Location: Chicago (Open to Remote) with client travel Type: Full-time Reporting: Works directly with the founder and alongside the Engagement Manager on active client accounts What You'll Do - Own technical delivery end-to-end: scoping, building, testing, and iterating without needing a PM to manage the process. - Design and deploy LLM workflows, RAG pipelines, and AI agent configurations tailored to specific CRE operational problems. - Sit in on client discovery sessions to ask the technical questions that shape what gets built. - Translate ambiguous client briefs into precise technical scopes with clear inputs, outputs, and success criteria. - Ship a working solution within the first week of every engagement. Momentum is the product. - Document everything: prompts, pipelines, configurations, and integration patterns that become reusable platform IP. - Feed field learnings back into the platform — agent library, onboarding templates, and delivery playbooks. What We're Looking For Required - 3–7 years of hands-on experience building and deploying AI systems in production — not demos or prototypes that never shipped. - Deep practical fluency with LLM APIs and orchestration frameworks: OpenAI, Anthropic, AWS Bedrock, LangChain, or equivalents. - Demonstrated ability to build RAG pipelines, agent systems, and data integration workflows independently. - Comfortable working directly with clients — you can sit across from a VP of Asset Management, understand what they need, and build it. - Autonomous and self-managing: you resolve your own blockers and keep the build moving. - Fast learner who will go deep on CRE workflows and find that interesting, not tedious. Preferred - Experience in a Palantir-style forward-deployed or embedded engineering role, technical consulting, or an early-stage startup. - Prior exposure to commercial real estate, proptech, or adjacent industries. - Experience integrating AI systems with enterprise data environments: property management, financial platforms, lease administration. - Track record of building reusable technical infrastructure, not just one-off solutions. How Engagements Work (3-Phase Model) Discovery (Weeks 1–3) - Stakeholder interviews - Process mapping and current-state assessment - Pain point prioritization and use-case selection - Output: prioritized roadmap + technical briefs + success metrics Build (Weeks 4–8) • Partner with the Engagement Manager on backend data work and AI solution configuration • Validate hypotheses with client stakeholders through iterative check-ins • Output: configured solution(s), implementation plan, and readiness checklist Implementation & Expansion (Ongoing) • Live deployment and adoption support • Measurement and iteration against success metrics • Identification of next use cases in partnership with the Engagement Manager • Output: sustained outcomes and account growth plan What Makes This Role Different Most AI engineering roles sit behind a product manager, a solutions engineer, and a client success layer. This one does not. You will work with a small founding team, directly with clients, on problems that have genuine financial consequence in one of the largest asset classes in the world. The FDE role at Fifth Wall Analytics is closer to the Palantir model than a typical AI engineering position: you are expected to own the technical outcome, understand the business problem deeply enough to challenge the brief, and build things that actually get used by the people who run billion-dollar portfolios. In return, you will accumulate an unusually deep cross-section of CRE operational knowledge, a reusable technical platform that compounds with every engagement, and — for the right candidate — a meaningful equity stake in a business being built at the right moment in a market that has barely started its AI transformation.