AI Product Builder

Motivosity•Lehi, UT

About The Position

Motivosity is seeking an AI Product Builder to transform its platform into an AI-first solution by developing and launching practical AI features and internal tools. This role emphasizes product ownership and decision-making, allowing individuals close to the code and customer problems to drive innovation. The focus is on shipping software that delivers tangible value, such as saving time and improving quality, rather than just creating demos. The company aims to be a technology leader in the AI-driven recognition and rewards space.

Requirements

Strong full-stack engineering experience and the ability to own production-quality code.
Ability to independently own projects from discovery through production.
Strong product judgment and the ability to make decisions without waiting for perfect requirements.
Strong backend, frontend, database, and API skills.
Experience building real software, not just prototypes.
Practical experience using AI tools to accelerate engineering work.
Hands-on experience with LLMs, agents, retrieval/RAG, structured outputs, or AI workflow automation.
Comfort working directly with customers, internal users, PMs, designers, and business leaders.
Strong debugging skills across the entire stack.
Good judgment around security, privacy, permissions, and customer trust.
Clear communication with both technical and non-technical teams.

Nice To Haves

Java / Spring Boot experience.
TypeScript, Angular, React, or similar frontend experience.
AWS or cloud deployment experience.
Experience with OpenAI, Anthropic, Gemini, Bedrock, LangChain, LangGraph, Langfuse, LangSmith, Braintrust, or similar tools.
Experience building or consuming MCP servers / connectors.
Experience in B2B SaaS, HR tech, customer support, customer success, or internal automation workflows.

Responsibilities

Own AI-powered product and internal workflow projects from start to finish.
Turn ambiguous ideas into working software without needing every requirement defined upfront.
Make product and technical decisions based on customer value, feasibility, risk, and speed of learning.
Build across the full stack: frontend, backend, APIs, databases, integrations, AI workflows, tests, and deployment.
Use AI tools as the default execution layer for design, coding, testing, debugging, documentation, and analysis, reviewing and approving the results.
Build practical AI features using LLMs, structured outputs, retrieval, agents, and tool-calling.
Create simple evaluations and feedback loops to verify AI features.
Monitor usage, quality, cost, latency, errors, and user feedback after launch.
Work directly with product, engineering, support, customer success, and leadership to identify high-value problems.
Ship small, useful things quickly and iterate based on real usage.