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 functionality based on actual usage
Balance speed with long-term maintainability

You should be comfortable operating where pro

AI Product Engineer (Full Stack) at Pavago