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

1. 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.
- 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 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.

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 an

📌 [HCM] AI Product Builder (Maiori)
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