AI Product Builder

About Motivosity

Motivosity is an employee recognition rewards and engagement platform. We help companies build cultures where people feel seen connected and appreciated through peer-to-peer recognition awards and milestones spending accounts manager tools and eNPS surveys that track how engagement moves over time.

Our engineering mission is to delight customers through unparalleled speed quality and reliability. That commitment is concrete: rewards and recognition are delivered on time to the right person with the correct reward; every financial transaction is recorded and reconciled; and the platform is built to scale to companies of 100000 employees without disruption.

About the role

Were hiring an AI Product Builder to help us turn Motivosity into an AI-first platform and to ship the practical AI features and internal tools that prove it.

Motivosity is putting more product ownership and decision-making into the hands of the people closest to the code the customer problem and the technical possibilities. This is not a narrow frontend backend ML or prompt-engineering role. Were looking for someone who can own a full product workflow end-to-end and use AI to move faster fill gaps prototype quickly and ship useful software.

The goal is not flashy demos. The goal is to ship software that saves time improves quality and creates measurable value for our customers and internal teams in a recognition-and-rewards space where we intend to be the technology leader for AI.

What youll do

• 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 your default execution layer for design coding testing debugging documentation and analysis then apply your own judgment to review and approve the result.
• Build practical AI features using LLMs structured outputs retrieval agents and tool-calling where appropriate.
• Create simple evals and feedback loops to verify AI features actually work.
• Monitor usage quality cost latency errors and user feedback after launch.
• Work directly with product engineering support customer success and leadership to find high-value problems.
• Ship small useful things quickly and iterate based on real usage.

What were looking for

• 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 especially important given we handle company budgets employee wallets and PII.
• Clear communication with both technical and non-technical teams.

Nice to have

• 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.

If youre an engineer who wants to own real problems end-to-end use AI as a force multiplier and help define how AI shows up in the recognition space wed love to talk!