Senior Full Stack Product Engineer

CubX•Lakewood, NJ

About The Position

CubX is seeking a hands-on Senior Full Stack Product Engineer to take business needs from concept to production. The role combines full-stack engineering with product thinking and design judgment. The engineer will work directly with business leaders and users to understand problems, design thoughtful solutions, and build polished internal applications that improve the operation of CubX’s managed service business. AI-assisted development is a core part of the role, with the engineer responsible for architecture, quality, security, and the final product while using AI coding tools to accelerate professional software development. The role owns work from stakeholder discovery and product design through architecture, implementation, release, and continued improvement based on user feedback and production behavior. It involves building and maintaining full-stack applications with React, TypeScript, Node.js, and MongoDB, and partnering with DevOps on Docker, CI/CD, Azure deployments, and production reliability. During the interview process, candidates will be asked to present two examples of software they delivered using AI-assisted development. At least one example should cover their work from stakeholder discovery and original product/design thinking through development and production release. Working demonstrations, anonymized case studies, and redacted materials are welcome.

Requirements

A track record of leading discovery and personally taking products or substantial workflows from an ambiguous need through visual design, architecture, implementation, production release, and continued improvement.
Deep hands-on proficiency in TypeScript, React, and Node.js, including the ability to explain, debug, and maintain the full application and AI-generated code.
Strong practical experience with MongoDB, Docker, Azure, Git, and CI/CD.
Experience with REST APIs, service boundaries, authentication, authorization, and automated testing.
A portfolio or case studies showing visual direction personally created and implemented.
Strong product judgment, stakeholder communication, and ability to simplify complex workflows.
Repeatable professional use of AI coding tools and agents, supported by production work personally shipped.
Substantial hands-on experience with one or more AI coding tools such as Claude Code, Cursor, OpenAI Codex, or GitHub Copilot.
Ability to demonstrate effective agentic coding, practical context engineering, and independent verification of AI output.

Nice To Haves

Experience building AI-powered application features using LLM APIs, structured outputs, tool calling, retrieval, or model evaluations.
Experience improving AI-development workflows through reusable agent instructions, automated checks, or Model Context Protocol (MCP) integrations.
Experience with MSP operations, enterprise administration, or internal business applications.

Responsibilities

Lead stakeholder discovery to understand business problems, requirements, workflows, and constraints.
Translate complex needs into clear user journeys, product designs, and technical solutions.
Create wireframes and prototypes and establish the visual direction for intuitive, polished interfaces.
Design application architecture, data models, APIs, integrations, permissions, and workflows.
Build and maintain full-stack applications using React, TypeScript, Node.js, and MongoDB.
Partner with DevOps on Docker, CI/CD, Azure deployments, and production reliability.
Use AI tools in planning, development, testing, debugging, refactoring, and documentation.
Review and verify AI-generated code rather than treating it as production-ready by default.
Own solutions through release and continue improving them based on user feedback and production behavior.
Demonstrate professional use of AI coding tools, including defining tasks and acceptance criteria, guiding work across files and services, inspecting changes, and correcting approaches.
Provide agents with relevant requirements, repository context, architectural constraints, and examples; maintain concise project instructions and reusable workflows where useful.
Verify AI output through code review, type checks, appropriate automated tests, and direct application inspection; identify invented APIs, missed requirements, security issues, and regressions.
Use judgment about when to delegate work to AI, investigate directly, or reject or rework a proposed solution, while balancing capability, cost, speed, and reliability.
Handle company and customer information, credentials, tool permissions, and production access carefully when using AI tools.