Context: Building in the AI Era
We are scaling product development alongside the evolution of generative AI. In this role you turn ambiguous intent into working prototypes and high-fidelity handovers to engineering. You operate in a paradigm shift toward meta-engineering: orchestrating generative AI, prompts, and tools—not only hand-coding from static specs—while our products address B2B back-office domains (e.g., accounting, HR) and rising expectations for clarity, speed, and a credible path to scale.
About the role
An implementer who takes direction from product and domain experts, uses generative AI (LLMs), no-code/low-code, and hands-on engineering to move from “what to build” to working prototypes and initial implementations in days—not weeks. This role bridges product, UX, and engineering: you shape the “how” at maximum speed, then hand off testable, specified work to the broader engineering organization.
This role expects a strong security mindset, with a clear understanding of data privacy, credential protection, device security, and the responsible use of corporate systems, tools, and accounts.
Responsibilities
- Work from business requirements and constraints set with domain experts; design prompts and AI-assisted workflows and translate needs into clear system and UX specifications.
- Rapid prototyping (UI/UX mockups, thin vertical slices, foundational implementations) using AI tools, no-code/low-code, and code where it accelerates outcome.
- Run hypothesis validation cycles on prototypes; deliver high-fidelity handovers (behaviour, data contracts, non-functional notes) to engineering teams.
- Decode legacy specifications and extend existing products using AI-assisted analysis and implementation where appropriate.
- Continuously evolve how the product team builds—tooling, templates, and practices—as models and platforms change.
Requirements:
Full-stack engineering depth (required)
You are not only a prompt author: you ship full-stack software and can own thin slices end-to-end when the prototype must be real.
- End-to-end feature development: backend services and SPAs; comfort moving across the stack for prototypes and v1 implementations.
- Backend: strong hands-on skills in Java or Ruby on Rails; RESTful APIs; sound approach to microservices and backward compatibility where relevant; performance, concurrency, and clean structure in code.
- Frontend: solid experience with modern frameworks such as React.js, Next.js, Vue.js, Nuxt.js, or Angular.js; strong JavaScript, TypeScript, HTML, and CSS; awareness of testing (e.g. Jest/Mocha) and quality (linting, reviews).
- Data: strong SQL skills and query tuning; ACID-aware design for transactional behaviour in prototypes.
- Delivery: CI/CD (e.g. GitHub Actions, CircleCI, or similar), Git workflows, code review habits; AWS and Docker for deployable prototypes; Kubernetes exposure is a plus.
- Reliability and security in scope of the prototype: authentication/authorization patterns, JWT/OAuth2 at a level appropriate to demos and handover notes; SLO/SLI thinking and observability hooks where the prototype will graduate.
- Leadership of craft: contribute to architecture for your domain verticals; mentor others; incident mindset (when things break, fix and document).
Required experience and skills (must have)
- Substantial software engineering and/or product-minded UI/UX delivery—you build things yourself, not only describe them. 8+ years in software development including both backend and frontend experience is the target bar for this principal-level scope.
- Generative AI: Enthusiastic, daily use of generative AI and advanced AI tooling to streamline work and materially accelerate delivery, combined with deep practical understanding of LLMs and proven application in product or internal delivery (not toy prompts only).
- Product mindset: Proven track record translating high-level product requirements into detailed requirements and comprehensive technical requirements through close partnership with Product Managers and domain stakeholders.
- Communication: Strong verbal and written English for clarity and alignment in distributed, multinational product engineering teams.
- Prototyping and product iteration using AI tools and no-code/low-code, grounded in user and operational workflow understanding.
- Comfort with ambiguity: ship “something that works first,” then refine in tight loops (agile execution).
- Track record as a central technical contributor on product initiatives that reached users and business outcomes.
- Agile familiarity (Scrum/Kanban); strong problem-solving across technical and product uncertainty.
Preferred (nice to have)
- Micro-frontends, state machines, advanced Java or Ruby on Rails concurrency.
- Kubernetes, Kafka/RabbitMQ, Prometheus/Grafana/ELK or similar.
- DDD, Clean/Hexagonal architecture; experience in accounting, finance, or other domain-heavy B2B products.
- Open-source or strong continuous learning profile.
- Japanese language skills (not required) — a strong plus for collaboration with Japan-based teams, product, and stakeholders.