AI Product Engineer,Internal Tools Job Calabasas area,California USA,IT/Tech

Planet Art is seeking an AI Product Engineer, Internal Tools to help build the next generation of AI-powered internal tools, prototypes, automations, and workflows across the company.

This is a hands‑on builder role working directly with the Head of AI, engineering team members, and business departments across Planet Art. The ideal candidate is a highly effective AI‑native builder who can use tools like Cursor, Claude Code, Codex, ChatGPT, and other modern AI development workflows to quickly turn ambiguous business problems into useful software.

This role is not just about taking requests and building exactly what was asked for. We are looking for someone who can work directly with internal users, understand the real workflow problem, ask sharp questions, prototype quickly, translate feedback into better tools, and know when to simplify, iterate, or push back. Strong UX instincts, technical judgment, communication skills, and a high‑agency mindset are essential.

IMPORTANT: A public portfolio is required. Candidates must include links to publicly reviewable portfolio work, such as live apps, websites, Git Hub repositories, demos, case studies, or shipped tools. Applications that do not include portfolio links will not be considered.

PLEASE NOTE: Candidates must be local to or willing to relocate to the Calabasas area as we operate on a hybrid work model (3 days onsite, 2 remote).

What You'll Do

– Key Responsibilities

Build AI‑powered internal tools, prototypes, dashboards, automations, and workflow applications for teams across Planet Art
Work directly with departments and internal users to understand pain points, observe workflows, gather feedback, and translate needs into practical software solutions
Use AI‑assisted development tools such as Cursor, Claude Code, Codex, ChatGPT, and similar platforms to rapidly prototype and iterate
Partner closely with the Head of AI and a small team of software engineers to identify high‑impact opportunities for internal tooling and AI enablement
Create functional prototypes quickly, validate them with users, and refine them based on real feedback and usage
Design and improve prompts, prompt chains, structured outputs, agent workflows, and AI‑assisted processes for internal business use cases
Build lightweight full‑stack applications, scripts, integrations, and automations using modern web and AI development tools
Balance speed with quality by knowing when a prototype is good enough, when it needs hardening, and when engineering support is required
Translate vague or incomplete stakeholder requests into clear requirements, user flows, acceptance criteria, and implementation plans
Improve user experience through thoughtful workflow design, clear interface choices, concise copy, and practical usability improvements
Document tools, workflows, prompts, assumptions, and handoff notes so internal teams and developers can understand and maintain what was built
Stay current with emerging AI coding tools, prototyping platforms, LLM capabilities, and applied AI workflows

What You Should Have – Skills, Qualifications, and Requirements

Publicly reviewable portfolio work is required; applications without portfolio links will not be considered
Demonstrated ability to build and ship functional apps, websites, prototypes, automations, or internal tools
Strong hands‑on experience with AI‑assisted development tools such as Cursor, Claude Code, Codex, Git Hub Copilot, ChatGPT, v0, or similar tools
Practical full‑stack development ability, especially with modern web technologies such as JavaScript, Type Script, React, Next.js, Python, APIs, databases, or similar tools
Strong prompt engineering skills, including the ability to design, test, refine, and document prompts for real business workflows
Ability to work directly with non‑technical users, gather feedback, identify the real problem, and convert that feedback into product improvements
Strong UX and product instincts, with the ability to make tools simple, useful, and easy for internal teams to adopt
Technical judgment to review AI‑generated code, debug issues, avoid fragile implementations, and escalate appropriately when production…