AI Builder

Job description

Everfield acquires and holds European software companies for the long term. We own 100% of the Business Units (BUs) we buy, but we do not run them directly. Each BU is led by its own CEO. We support and steer the portfolio through a small set of operating ratios that translate strategy into measurable performance. We believe the software industry is going through a paradigm shift: from *tools that help customers do the job* to *agents that do the job for them*. Our portfolio has tens of companies, around 500 product ideas in the pipeline, and tens of thousands of customers across European verticals. The companies that adopt the agent paradigm first will define the next decade of their categories. This is one of the most exciting product surfaces in European B2B SaaS right now, and it needs a builder. We're hiring a **AI Builder** to be the hands-on engineering force inside Everfield's product team. You'll work as the technical counterpart to the Product Lead: jumping into BU codebases, prototyping with agentic coding tools, and shipping agent-first features end to end on real products with real customers. You will not be short on problems to solve. We have a deep pipeline of customer-validated ideas, a portfolio of BUs that are eager to be first movers, and the freedom to prototype in days rather than quarters. What you will do - Build agent-first products end to end inside the codebases of Everfield's BUs. Spec, prototype, ship, iterate, hand off. - Drop into any BU codebase, get productive in days, and produce working software fast. Backend systems, data platforms, workflow tools, and the occasional UI heavy product. - Run agentic coding (Claude Code, Codex, SpecKit and whatever comes next) as the default mode of work, not as a novelty. Where AI tooling does not yet exist for a problem, you build it. - Prototype directly with BU customers and iterate based on what they actually use, not what they say in interviews. - Work side by side with BU engineering teams on their own codebase so the BU has first-hand experience of how the product was built and can maintain and extend it after you move on. - Translate validated ideas into shipped products in weeks rather than quarters. - Hand off cleanly: production-ready code, the documentation a BU engineering team needs to keep it alive, and a short technical narrative the BU CEO can use. - Spot the next build opportunity while you're delivering the current one. The Product Lead will run customer discovery, but you will be in the room and your technical instinct on what's actually buildable matters. - Help codify what works into a repeatable build playbook so the model can scale to additional builders over time. What we are looking for - Senior engineer who has shipped real software to real users. An engineering degree or self-taught, either is fine. What matters is the body of shipped work. - Founder or near-founder experience. You have founded or co-founded a tech startup (B2B SaaS preferred but not required), or operated as a near-founder with full ownership of a product. You know what it costs to ship. - AI-native ways of working. Daily, hands-on use of agentic coding tools. You build with AI by default. You have a clear point of view on where these tools are strong, where they're brittle, and how to wire them into a real codebase without creating maintenance debt. - Polyglot pragmatism. You can work across stacks. You've shipped in at least two languages in production. You're not religious about any of them. You can read an unfamiliar codebase and be useful inside a week. - Comfort with messy, legacy code. Many of our BUs run mature, established products with rich histories. You can work inside that reality and choose intelligently between greenfield, brownfield, and "greyfield" (a clean rebuild wired into existing UI and APIs). - Direct customer exposure. You're comfortable demoing to customers, watching them use what you built, and changing it on the spot. - 3 to 8 years of experience, typically in engineering, founding, or hybrid product/engineering roles. Probably did a stint at a larger company and got frustrated. - Comfort with ambiguity and jumping between BUs, codebases, and customer segments without getting stuck. Strongly preferred - Experience operating across multiple companies, portfolios, or accelerator-style environments rather than spending years on a single product. - General familiarity with B2B SaaS fundamentals. - Working in English at the executive level. Other European languages are a bonus. - Exposure to post-acquisition or M&A integration environments. Nice to have - A failed, acqui-hired, or still-running-on-the-side startup on your CV. Scar tissue is welcome. - Public technical work: GitHub, conference talks, technical writing, open-source contributions, or a personal site of side projects that show how you think. - Experience integrating LLMs into production systems beyond the demo stage: evals, guardrails, retrieval, tools, cost control. How we work We are AI-native and practice-led. We don't write 50-page PRDs. We run SpecKit, prototype, and put working software in front of customers as fast as we can. We co-build with BU teams on their codebases, never as a black box, so the company has first-hand experience of how the product was made. You will work closely with the Product Lead as a true peer on the build, not as a downstream implementer. The pair is the smallest unit, and the smallest unit ships. Two people working in parallel close the loop between "what the customer needs" and "what the prototype does" faster than any larger team can. You are not expected to set portfolio priorities, choose which BUs to engage, or own GTM. Priorities come from the HQ Product team, Impact-Confidence-Ease scoring, and BU and Group Director alignment. GTM stays with the BU, supported by HQ Revenue. Your job is to ship, and to spot the next thing worth shipping while you're at it. This is a builder's role. If you want to manage a team, this isn't it. If you want to ship agent-first products into tens of European software companies, with full codebase access and a tight feedback loop with real customers, it is. Process - TA screening call (30min) - Interview with Product Lead Operations (1 hour) - Interview with AI Lead (30 min)