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
AI & System Design
Design and orchestrate AI agents, skills, and multi-step workflows integrated into micro-services and frontend architectures.
Translate business requirements and domain constraints into explicit UX flows, data contracts, and technical specifications.
2. Full-Stack Engineering & Velocity
Develop end-to-end applications and features using Java/Kotlin, Spring Boot, and React JS.
Build rapid, functional prototypes and vertical slices using a hybrid of custom code, AI-assisted tools, and low-code solutions to validate concepts quickly.
3. Delivery, Scale & Modernization
Package and deliver production-ready, testable code and documentation for seamless integration into the broader engineering ecosystem.
Analyze, refactor, and extend legacy codebases using AI-driven diagnostic and coding tools.
Drive continuous improvement across the engineering stack by pioneering new AI development methodologies, templates, and platforms.
4. Key Enhancements Made
Technical Integration: Linked standard engineering tools (Spring Boot, React) directly to AI workflows so candidates know they will write real code, not just build prompt chains.
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 Kotlin; RESTful APIs; sound approach to micro-services and backward compatibility where relevant; performance, concurrency, and clean structure in code.
Frontend: solid experience with modern frameworks such as [protected info], [protected info], [protected info], [protected info], or [protected info]; strong JavaScript, TypeScript, HTML, and CSS; awareness of testing (e.g. Jest/Mocha) and quality (linting, reviews).
Data: strong SQL skills and query tuning; ACIDaware 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)
Generative AI:
+ Agentic Frameworks & Workflows: Hands-on experience designing and orchestrating AI agents, custom skills, and multi-step workflows (e.g., via LangChain, LlamaIndex, Semantic Kernel, or similar frameworks/tools).
+ AI-Assisted Development: Advanced proficiency with AI-driven coding tools (e.g., GitHub Copilot, Cursor, Claude, ChatGPT) to accelerate system design, debugging, and code generation.
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.
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 Golang or Java 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.