Director, Engineering

Build genuine understanding of what they are trying to accomplish and let that shape how your teams think and build.

Product solutioning.

Work alongside Product and users to translate real workflow problems into solutions that stick.

The solutions you build will be used on a shop floor by someone who needs them to work every time.

Bring engineering judgment to what gets built: knowing when a targeted feature solves the problem, when a deeper architectural change is what is actually needed, and when the right answer is to simplify rather than add.

Technical architecture partnership.

Work with the CTO and engineering leadership on platform decisions across our Azure and MongoDB Atlas stack and the move toward agentic surfaces.

Full partner in the decisions and fully accountable for how your teams deliver them.

AI era engineering culture.

Every stage of the development cycle, from a design whiteboard to a production deploy, should have AI making the team faster and the output better.

You build that culture and you go first.

Engineering standards.

Leaves the codebase, the process, and the people in better shape than you found them: CI/CD, code review culture, observability, and incident learning treated as foundations, not afterthoughts.

Cross-functional execution.

Partner with Product, Data, and Customer Success to ship what customers depend on.

Bring what you learn in the field back into how your teams build.

What You Bring AI forward and pragmatic engineering.

Has led a team shipping AI enabled product features that customers depend on in production.

The bar is not a side project.

It is something a customer paid for that would not exist without AI.

Knows what it takes to keep AI reliable in production, makes the right build versus buy call, and focuses on shipping AI customers trust rather than infrastructure for its own sake.

Technical depth.

Cloud native SaaS architecture with Azure experience preferred.

Comfortable in C# or .NET.

MongoDB or document database experience is a plus.

Experience. 10 or more years in software engineering including direct experience building and shipping AI enabled products, with 5 or more in engineering leadership at a SaaS product company.

Has shipped in an environment where the customer base was growing fast enough that a bad release had real consequences.

Has taken on a team, nurtured it to higher impact and done it without losing what was already working.

Has hired at least one person who became exceptional and can explain exactly why.

Customer and product orientation.

Can sit in a customer conversation, find the real problem beneath the stated request, and carry that signal back to the teams.

Thinks first about what outcome the customer needs, then about how to deliver it.

Measures success in value shipped, not tasks completed.

Leadership experience, including strong communication skills and ability to work with a distributed team and cross functional teams.

Domain (strong plus).

MEP, fabrication, or construction technology experience lets you challenge product and architecture decisions with domain intuition and build customer credibility faster.

Nice to Have Experience with Autodesk or similar Exposure to manufacturing, supply chain, or field operations software.

Hands on experience with LangGraph, MCP, or agentic orchestration frameworks.