[Hcm] Ai Product Builder (Java, Ai)
pWe 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.
/p h3About the role /h3 pAn 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.
/p pThis 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.
/p h3Responsibilities /h3 p1.
AI System Design /p ul liDesign and orchestrate AI agents, skills, and multi-step workflows integrated into micro-services and frontend architectures.
/li liTranslate business requirements and domain constraints into explicit UX flows, data contracts, and technical specifications.
/li liDevelop end-to-end applications and features using Java/Kotlin, Spring Boot, and React JS.
/li liBuild rapid, functional prototypes and vertical slices using a hybrid of custom code, AI-assisted tools, and low-code solutions to validate concepts quickly.
/li liPackage and deliver production-ready, testable code and documentation for seamless integration into the broader engineering ecosystem.
/li liAnalyze, refactor, and extend legacy codebases using AI-driven diagnostic and coding tools.
/li liDrive continuous improvement across the engineering stack by pioneering new AI development methodologies, templates, and platforms.
/li /ul h34.
Key Enhancements Made /h3 ul liTechnical 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.
/li /ul pYou 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.
/p ul liEnd-to-end feature development: backend services and SPAs; comfort moving across the stack for prototypes and v1 implementations.
/li liBackend: 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.
/li liData: strong SQL skills and query tuning; ACIDaware design for transactional behaviour in prototypes.
/li liDelivery: 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.
/li liReliability 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.
/li liLeadership of craft: contribute to architecture for your domain verticals; mentor others; incident mindset (when things break, fix and document).
/li /ul h3Required experience and skills (must have) /h3 ul liGenerative AI: /li /ul p+ 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).
/p p+ 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.
/p ul liSubstantial 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.
/li liProduct 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.
/li liCommunication: Strong verbal and written English for clarity and alignment in distributed, multinational product engineering teams.
/li liPrototyping and product iteration using AI tools and no-code/low-code, grounded in user and operational workflow understanding.
/li liComfort with ambiguity: ship "something that works first," then refine in tight loops (agile execution).
/li liTrack record as a central technical contributor on product initiatives that reached users and business outcomes.
/li liAgile familiarity (Scrum/Kanban); strong problem-solving across technical and product uncertainty.
/li /ul h3Preferred (nice to have) /h3 ul liMicro-frontends, state machines, advanced Golang or Java concurrency.
/li liKubernetes, Kafka/RabbitMQ, Prometheus/Grafana/ELK or similar.
/li liDDD, Clean/Hexagonal architecture; experience in accounting, finance, or other domain-heavy B2B products.
/li liOpen-source or strong continuous learning profile.
/li liJapanese language skills (not required) — a strong plus for collaboration with Japan-based teams, product, and stakeholders.
/li /ul #J-*****-Ljbffr
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.
/p h3About the role /h3 pAn 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.
/p pThis 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.
/p h3Responsibilities /h3 p1.
AI System Design /p ul liDesign and orchestrate AI agents, skills, and multi-step workflows integrated into micro-services and frontend architectures.
/li liTranslate business requirements and domain constraints into explicit UX flows, data contracts, and technical specifications.
/li liDevelop end-to-end applications and features using Java/Kotlin, Spring Boot, and React JS.
/li liBuild rapid, functional prototypes and vertical slices using a hybrid of custom code, AI-assisted tools, and low-code solutions to validate concepts quickly.
/li liPackage and deliver production-ready, testable code and documentation for seamless integration into the broader engineering ecosystem.
/li liAnalyze, refactor, and extend legacy codebases using AI-driven diagnostic and coding tools.
/li liDrive continuous improvement across the engineering stack by pioneering new AI development methodologies, templates, and platforms.
/li /ul h34.
Key Enhancements Made /h3 ul liTechnical 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.
/li /ul pYou 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.
/p ul liEnd-to-end feature development: backend services and SPAs; comfort moving across the stack for prototypes and v1 implementations.
/li liBackend: 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.
/li liData: strong SQL skills and query tuning; ACIDaware design for transactional behaviour in prototypes.
/li liDelivery: 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.
/li liReliability 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.
/li liLeadership of craft: contribute to architecture for your domain verticals; mentor others; incident mindset (when things break, fix and document).
/li /ul h3Required experience and skills (must have) /h3 ul liGenerative AI: /li /ul p+ 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).
/p p+ 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.
/p ul liSubstantial 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.
/li liProduct 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.
/li liCommunication: Strong verbal and written English for clarity and alignment in distributed, multinational product engineering teams.
/li liPrototyping and product iteration using AI tools and no-code/low-code, grounded in user and operational workflow understanding.
/li liComfort with ambiguity: ship "something that works first," then refine in tight loops (agile execution).
/li liTrack record as a central technical contributor on product initiatives that reached users and business outcomes.
/li liAgile familiarity (Scrum/Kanban); strong problem-solving across technical and product uncertainty.
/li /ul h3Preferred (nice to have) /h3 ul liMicro-frontends, state machines, advanced Golang or Java concurrency.
/li liKubernetes, Kafka/RabbitMQ, Prometheus/Grafana/ELK or similar.
/li liDDD, Clean/Hexagonal architecture; experience in accounting, finance, or other domain-heavy B2B products.
/li liOpen-source or strong continuous learning profile.
/li liJapanese language skills (not required) — a strong plus for collaboration with Japan-based teams, product, and stakeholders.
/li /ul #J-*****-Ljbffr