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.

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 product requirements evolve and engineers are expected to contribute to how a problem should be solved, not simply execute tickets.

Debugging, Performance & Reliability

Own product quality after features are shipped.

You'll:

• Diagnose and resolve bugs
• Monitor system behavior
• Improve application performance
• Identify technical bottlenecks
• Strengthen system reliability
• Improve user experience based on production issues
• Reduce technical debt as the platform matures

Required Experience & Skills

Non-Negotiables

• Strong full-stack engineering experience across frontend and backend development
• Proven experience building and shipping production-level products
• Hands-on experience integrating AI/LLMs into real applications
• Strong understanding of:
• System architecture
• API design
• Data structures
• Application performance
• Experience with Supabase or similar backend platforms
• Understanding of practical AI limitations, including:
• Hallucinations
• Failure states
• Edge cases
• Output validation
• Strong knowledge of security best practices, including:
• Authentication
• Permissions
• Data protection
• Strong debugging and problem-solving ability
• Ability to work independently and take end-to-end ownership of product development

Nice to Have

• Experience building AI agents
• Experience designing AI automation workflows
• Familiarity with Claude or similar LLMs in production
• Experience building:
• SaaS products
• Operational platforms
• Workflow applications
• Experience taking products from MVP to production
• Experience scaling applications after initial launch
• Exposure to:
• Aviation
• Logistics
• Membership systems

What Makes You a Strong Fit

You:

• Are a builder, not just a feature contributor
• Can take an idea and independently turn it into working software
• Think across frontend, backend, data, AI, security, and infrastructure
• Have shipped real products that users depend on
• Understand that production AI requires more than calling an LLM API
• Design around AI failure modes rather