- 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.
How we work 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.
What you bring Engineering
- 5+ years of shipping and supporting software that real users depend on, not prototypes.
- Strong Python and SQL. Backend work is central to this role, and a second language such as TypeScript, Node.js, or Go is welcome.
- Enough frontend ability to put a usable interface on what you build, whether that is React and TypeScript, Streamlit or Power Apps, or an experience embedded in a tool people already use.
- REST APIs, webhooks, and event-driven integrations, and services that connect SaaS platforms reliably, including retries, idempotency, and failure handling.
- Relational data and data modeling, with working knowledge of document or vector stores and caching, and judgment about which fits.
- CI/CD, pull-request-based development, automated testing, and infrastructure as code. Azure is our cloud; AWS or Google Cloud translates.
- Daily use of AI coding tools such as Claude Code, Cursor, Codex, or GitHub Copilot, and a clear sense of where they need supervision.