Senior Full Stack Product Engineer

CubX Inc.•Lakewood, NJ

4d•$90,000 - $150,000

About The Position

CubX is seeking a hands-on Senior Full Stack Product Engineer to take business needs from concept through 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 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 responsibility for using AI coding tools effectively while retaining ownership of architecture, quality, security, and the final product. The engineer will lead stakeholder discovery, translate complex needs into user journeys and technical solutions, create wireframes and prototypes, establish visual direction, and design application architecture and workflows. They will build and maintain applications using React, TypeScript, Node.js, and MongoDB, partner with DevOps on Docker, CI/CD, Azure deployments, and production reliability, and use AI tools throughout software delivery. They will independently verify AI-generated code and own solutions through release and ongoing improvement based on user feedback and production behavior. Candidates should be able to demonstrate two examples of software delivered using AI-assisted development. At least one example should show involvement from stakeholder discovery and original product/design thinking through development and production release. Candidates should be prepared to discuss the business problem, design and architecture decisions, AI use, an AI-generated issue they identified, and what shipped and changed after release. Working demonstrations, anonymized case studies, and redacted materials are welcome.

Requirements

Proven experience taking products or significant workflows from an ambiguous business need through design, development, deployment, and ongoing improvement.
Strong hands-on experience with TypeScript, React, Node.js, and MongoDB.
Experience with Azure, Docker, Git, CI/CD, REST APIs, authentication, authorization, and automated testing.
Strong product judgment and ability to simplify complex business workflows.
Experience creating and implementing user interfaces, rather than simply working from someone else’s designs.
Strong communication skills and comfort working directly with business and technical stakeholders.
A track record of leading discovery and personally taking products or substantial workflows 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, CI/CD, REST APIs, service boundaries, authentication, authorization, and automated testing.
A portfolio or case studies showing visual direction personally created and implemented.
Substantial hands-on experience with one or more AI coding tools such as Claude Code, Cursor, OpenAI Codex, or GitHub Copilot, supported by production work personally shipped.
Effective agentic coding ability, including defining tasks and acceptance criteria, guiding work across relevant files and services, inspecting changes, and correcting approaches when needed.
Practical context engineering: providing agents with relevant requirements, repository context, architectural constraints, and examples.
Ability to independently verify AI output through code review, type checks, appropriate automated tests, and direct inspection of the application.
Ability to identify invented APIs, missed requirements, security issues, and regressions in AI-generated work.
Good judgment about when to delegate work to AI, investigate directly, or reject or rework proposed solutions, while balancing tool capability, cost, speed, and reliability.
Careful handling of company and customer information, credentials, tool permissions, and production access when using AI tools.

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 discovery with stakeholders 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 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 throughout planning, development, testing, debugging, refactoring, and documentation.
Review and verify AI-generated code rather than treating AI output as production-ready by default.
Own solutions through release and continue improving them based on user feedback and production behavior.
Use agentic coding practices: define tasks and acceptance criteria, guide work across relevant files and services, inspect changes, and correct the approach when needed.
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, automated tests, and direct application inspection; identify invented APIs, missed requirements, security issues, and regressions.
Exercise judgment about when to delegate to AI, investigate directly, or reject or rework a proposed solution, balancing capability, cost, speed, and reliability.
Handle company and customer information, credentials, tool permissions, and production access carefully when using AI tools.
Demonstrate two examples of software delivered using AI-assisted development during the interview process, including at least one taken from discovery and product/design thinking through production release.