Engagement Manager

As Engagement Manager, you will own 2-3 client relationships and lead engagements from initial discovery through implementation and ongoing expansion. You will sit at the intersection of consulting, AI solution design, and client success – translating ambiguous real estate problems into clear, executable plans and measurable outcomes.

What you’ll do

Own client relationships end-to-end, serving as the primary relationship manager and day-to-day point of contact.

Lead discovery engagements (typically 2–3 weeks):

Conduct stakeholder interviews across business, operations, and technology teams

Map current-state processes and decision flows

Identify, size, and prioritize pain points that are well-suited for AI intervention

Translate business needs into structured problem statements, technical briefs, and solution hypotheses in partnership with Product and Engineering.

Manage engagement scope, timelines, deliverables, and client communications with high precision and polish.

Present findings, recommendations, and progress updates to senior audiences (VP , C-suite) with confidence.

Drive implementation success by coordinating across internal technical teams and client stakeholders to support deployment and adoption.

Identify expansion opportunities over time by linking delivered outcomes to additional high-value use cases.

Serve as an internal feedback loop—bringing field insights back to improve the platform, packaging, and go-to-market motion.

Travel to client sites as needed for relationship‑building and on‑site discovery sessions.What we’re looking for

Required

7-10+ years of experience in tech enabled services consulting, strategy, or a high‑velocity, client‑facing role specifically in the commercial real estate domain

Demonstrated ability to elicit business requirements from non‑technical stakeholders and translate them into clear, testable problem statements.

AI‑native mindset: you actively use AI tools in your work and understand practical

capabilities, limitations, and adoption considerations.

Outstanding written and verbal communication: comfortable running executive meetings and producing crisp one‑pagers and client‑ready deliverables.

Strong organizational skills and comfort managing multiple engagements simultaneously.

Entrepreneurial orientation: energized by building new processes, templates, and playbooks in a lean environment.

Preferred

Experience at a Series A–C startup or in roles requiring significant cross‑functional ownership.

Familiarity with AI/ML solution configuration and/or data pipeline concepts (you don’t need to build them, but should be able to speak to them).

Track record of growing client relationships beyond the initial scope through demonstrated outcomes.

How engagements work (3‑phase model)
1. 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

2. Build (Weeks 4–8)

Partner with technical team 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

3. Implementation & Expansion (Ongoing)

Live deployment and adoption support

Measurement and iteration against success metrics

Relationship deepening and identification of next use cases

Output: sustained outcomes and account growth plan

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