AI Product Engineer (Full Stack)
AI Product Engineer (Full Stack)
AI/LLM Integration, Full-Stack Development & Product Engineering | Remote | U.S. Hours
Position Type: Full-Time, Remote
Working Hours: U.S. Business Hours
About the Role
At Pavago , one of our clients is hiring an AI Product Engineer (Full Stack) to build and scale a production-ready web application from the ground up.
This is a hands-on product engineering role , not a support or maintenance position.
You’ll take ownership across the entire product lifecycle:
- Frontend development
- Backend architecture
- AI/LLM integration
- APIs and data infrastructure
- Authentication and security
- Deployment and performance
- Product iteration and scaling
You’ll work closely with leadership to turn product ideas into working, production-ready software , ship quickly, learn from real usage, and continuously improve the platform.
If you’re a builder who can independently take an AI-powered product from concept → MVP → production → scale , this role is a strong fit.
What You’ll Own
Full-Stack Product Development — End-to-End
Own development of the product from initial architecture through production deployment.
You’ll:
- Build and launch a production-ready web application
- Own both frontend and backend development
- Design intuitive, responsive product experiences
- Build scalable application architecture
- Translate product requirements into working features
- Deploy, monitor, and improve the application
- Continuously iterate after launch based on user feedback and product needs
This role requires someone comfortable owning the entire technical product , rather than working within one narrow layer of the stack.
AI & LLM Integration
Design AI functionality that solves real product problems—not AI features added simply for novelty.
You’ll:
- Integrate LLMs such as Claude or similar models
- Design AI-powered product workflows
- Build reliable interactions between LLMs, application logic, and user data
- Structure prompts and outputs for consistent product behavior
- Handle edge cases and model failures
- Implement safeguards and validation around AI-generated outputs
You should understand practical LLM limitations, including:
- Hallucinations
- Unreliable outputs
- Context limitations
- Edge cases
- Failure handling
The goal is to build AI experiences users can actually rely on in production.
Backend Systems, APIs & Data Infrastructure
Build and manage backend infrastructure using Supabase or similar platforms .
You’ll:
- Design scalable APIs
- Build efficient data models and structures
- Manage application data and backend logic
- Connect frontend experiences to backend services
- Integrate external APIs and AI services
- Optimize database queries and application performance
- Build infrastructure capable of supporting continued product growth
Security, Authentication & Permissions
Build security into the product from the beginning.
Implement and maintain:
- Authentication
- User permissions
- Authorization logic
- Data access controls
- Secure API interactions
- Data protection practices
Ensure sensitive information is handled appropriately and that users only have access to the functionality and data they are authorized to use.
AI Agents & Automation
Where appropriate, build more advanced AI-powered systems such as:
- AI agents
- Multi-step AI workflows
- Automated operational processes
- Tool-calling workflows
- AI-assisted decision systems
Design these systems with appropriate validation and safeguards so automation remains reliable in real-world scenarios.
Product Collaboration & Rapid Iteration
Work directly with leadership to translate ideas into technical solutions.
You’ll:
- Understand product requirements and business objectives
- Recommend practical technical approaches
- Build prototypes quickly
- Turn successful prototypes into production features
- Ship frequently
- Gather feedback
- Refine functi