Operator-Engineer is the role Runpoint was built around.
Most companies split the work into strategy, product, engineering, enablement, and change management. Then they wonder why AI projects turn into theater. We want people who can carry the whole arc. Understand the business. Find the constraint. Build the system. Watch users touch it. Fix what breaks.
You should be strong enough technically to work in the repo, practical enough to ignore fancy solutions when the boring one works, and curious enough to keep pulling on the business problem until the shape is obvious.
What you will do
- Embed with a client and learn how the business actually runs.
- Turn messy workflows into production AI systems, automations, internal tools, and operating routines.
- Use Claude Code, Codex, and other AI-native tools as your normal build environment.
- Interview stakeholders, review process evidence, write requirements, build prototypes, ship to production, and keep improving the system after real usage.
- Know when to bring in a specialist instead of pretending every problem is yours to solve.
What we need
- You have shipped real things with AI tools in the last six months.
- You can hold context with a CEO, an ops lead, and an engineer without becoming three different people.
- You can read code, reason about architecture, and make good tradeoffs without ceremony.
- You think in business outcomes first. The tool choice comes later.
- You are comfortable with ambiguity and allergic to waiting around.
Strong signals
If any of these describe you, the conversation will move quickly.
- You have a weird background that makes sense only after someone sees your work.
- You have built side projects because you could not leave the problem alone.
- You know when AI output is useful and when it is confidently wrong garbage.
- You prefer a small team with high trust to a large team with immaculate process artifacts.