Hcm Ai Product Builder (Java, Ai)
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.Tutti i potenziali candidati sono invitati a leggere con attenzione i seguenti dettagli prima di inviare la propria candidatura.About the roleAn 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.Responsibilities1. AI & System DesignDesign 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.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.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 MadeTechnical 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.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.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. xysqume Japanese language skills (not required) — a strong plus for collaboration with Japan-based teams, product, and stakeholders.#J-18808-Ljbffr
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 MadeTechnical 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.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.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. xysqume Japanese language skills (not required) — a strong plus for collaboration with Japan-based teams, product, and stakeholders.#J-18808-Ljbffr