AI Tooling Engineer, Qventus

• 1–3 years building software — backend services, internal tools, automations, or API-driven apps. Internships and co-ops count
• Strong in TypeScript and/or Python
• You've built something with LLMs — an agent, a RAG pipeline, a copilot, a prompt-driven tool — through work, a side project, or coursework. We care that you've shipped, not where
• Comfortable wiring systems together: APIs, webhooks, SaaS integrations, async workflows
• You can talk to non-technical stakeholders, figure out what they really need, and drive adoption — this matters as much as the code
• You move fast under ambiguity and iterate from real feedback

Desirable

• We're hiring Engineers dedicated to building AI-powered tools that change how work gets done inside the company
• You'll embed with teams across operations, customer success, and finance, find the work that's slow and manual, and ship AI agents and automations that actually get adopted
• This is a build-fast, talk-to-humans, own-the-problem role - not a research role and not a pure-infrastructure role. You'll define what "internal AI" looks like here from scratch
• You're energized by ambiguity and ownership. You'd rather ship a rough tool this week, watch someone use it, and improve it than spend a month on a perfect spec
• You like sitting with a non-technical teammate, understanding their actual workflow, and turning it into something that saves them hours. You're comfortable being the only person who owns a problem end to end
• You'll partner closely with the CEO and founders on identifying the highest-leverage operational problems to solve and shaping where internal AI goes next. This is a rare amount of executive access to an engineering role
• You’ll work alongside functional leaders across the business to understand their priorities, surface opportunities , and align on what to build. Your work will be visible and leadership level from day one
• Operations, Customer Success, Finance, People/Talent Acquisition, Engineering, IT and Data teams, these are your internal customers and collaborators. You'll embed with them, and learn their workflows and ship tools they actually use
• Examples of the kind of work waiting for you:
• An agent that drafts customer success replies from our knowledge base and ticket history
• A Slack-based workflow that routes and pre-fills finance approvals
• An internal eval harness so we can trust the LLM features we ship
• A tool that lets the ops team query [internal system] in plain language
• You won't do all of these at once — you'll pick the highest-impact one, ship it, and move to the next
• What success looks like
• First few weeks: shipped your first internal tool and it's in active daily use by a business team
• Within 2 months: 2–3 tools live, and stakeholders are coming to you with ideas
• By month 3: you've set the team's baseline practices for evals, prompt management, and safe LLM deployment — the foundation the function grows on