Agent Engineer (Builder)

Omninet Capital is a vertically integrated private real estate investment and property management firm. The Agent Engineer will design, build, deploy, and maintain AI agents and low-code automations that address operational workflows across acquisitions, accounting, property management, HR, and leasing. The role partners with business stakeholders, integrates agents with existing systems, measures efficiency gains, and supports secure adoption by non-technical users.

Responsibilities:

• Interview stakeholders across Acquisitions, Accounting, Operations, HR, and Leasing to map current manual workflows
• Design, build, and deploy AI agents (skills, plugins, and connectors on our AI platforms, plus rules-based automations) that automate document review, data entry, reporting, underwriting support, and internal Q&A
• Integrate agents with existing systems (Microsoft 365, SharePoint, Outlook, Yardi, Rippling) using available connectors and low-code/no-code tools
• Package finished tools so non-technical users can run them easily, with short how-to guides
• Work with regional and on-site property management teams to identify high-friction tasks (maintenance ticketing, resident communications, lease abstraction, vendor invoicing, compliance tracking)
• Deploy automation and AI agents that reduce manual data entry and turnaround time across the ~8,500-unit multifamily portfolio and commercial assets
• Travel occasionally to properties when on-site requirements gathering or rollout support is needed; most discovery happens remotely over Teams
• Monitor, maintain, and iterate on deployed agents; track efficiency gains (hours saved, error reduction, cost avoidance)
• Document workflows and maintain a library of reusable automation components
• Test each automation with its business owner before release, and keep a person in the loop for anything sent outside the company
• Work within IT's security model: least-privilege access, approved connectors, and service accounts
• Stay current on AI/agent tooling and recommend new use cases, including when a workflow is ready for more advanced, API-based agents
• Deliver several working automations for real teams, such as lease renewal letter drafting and broker deal intake
• Measure results (hours saved, turnaround time, error rates) and share what worked and what didn't
• Build a reusable library of skills, templates, and connectors that makes each new automation faster
• Use what we learn together to shape the program's next stage