AI Solutions Engineer

Havenpark owns and operates manufactured housing communities across several states, supported by a lean central team. Selling agents, community managers, accountants, and marketers all do repetitive work every day across many communities, and AI gives us that time back. It already runs in production in parts of our sales, marketing, and finance work, and this role is how it reaches the rest of the business.

You will sit with the people doing the work, learn how their day actually runs, and then build and operate the AI companions, agents, and automations that change it. The foundation is in place: governed data in Snowflake, a semantic layer, and a modern Microsoft and Azure environment. This is an internal engineering role rather than a customer-facing one, with direct access to the executives who sponsor the work and the users who live with it, and what you ship gets measured in hours returned, adoption, and reliability.

• Assess how work is actually performed, role by role, and prioritize where AI removes steps rather than only speeding them up, weighing hours returned, error reduction, and how many people the change reaches.

• Design, build, and ship AI companions, agents, and workflow automations from prototype through production, for users including selling agents, community teams, accounting, and marketing.

• Integrate with the systems where work happens: HubSpot, Microsoft 365, Snowflake, our property management and finance platforms, and vendor APIs. Choose the runtime that fits the workload rather than forcing everything into one tool.

• Build on the governed data models and semantic layer the analytics team maintains. You consume them rather than own them, and you tell us what agents need.

• Maintain reusable patterns, prompts, tools, and evaluation sets so the third solution is faster to build than the first.

• Own evaluation, monitoring, exception handling, and escalation for every production workflow. Define what good looks like before launch and know within hours when something drifts.

• Enforce human review for consequential actions. Anything touching a customer, a resident, money, or a record of record keeps a person in the loop until the evidence says otherwise.

• Manage model usage and cost, ship through source control and pull requests, and document each solution well enough that someone else could support it.

• Keep the inventory of agents, prompts, and integrations current, including who owns each one and what it can access.

• Partner with function leaders to pick the right problems, agree on what success means, and confirm the value was realized after launch rather than at the demo.

• Embed with users while a solution is new. Watch how they use it, find where it fails or gets ignored, and iterate. Adoption is part of the job.

• Train users and their managers on what a solution does, what it does not, and how to escalate when it gets something wrong.

• Work within Havenpark's AI usage policy and bring new tools and use cases through the intake process, involving Legal and HR early when a use case touches applicant, resident, or employee data.

• Give every agent and integration its own identity with least-privilege access. No shared API keys, no over-permissioned service accounts, and no production credentials in code.

• Log what agents did, what data they used, and who approved consequential actions, so any outcome can be traced and explained.

Data first. AI here runs on governed, well-defined data. When an agent needs a metric or a record, it should return the same answer the executive dashboard does, and when the data is not there yet, we expect you to say so and help get it there.

AI-accelerated, human-owned. AI is a fundamental part of how we work and how a small team builds things that scale across the portfolio. Use it in your own work every day, and keep a person accountable for every consequential decision.

Ship small, then scale. We deliver in pull-request-sized steps, start in development, and prove reliability before widening the audience.

Nothing launches without an owner. A solution is finished when someone is responsible for monitoring it, handling exceptions, supporting users, and changing it as the business changes, and that someone is usually you.