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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 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 than assuming outputs will always be correct
Move quickly while maintaining strong engineering standards
Debug problems independently
Make thoughtful architecture decisions without overengineering
Enjoy working directly with leadership and translating business ideas into products
Take ownership from initial concept through production performance
What a Typical Day Looks Like
Your day may include:
Building a new frontend product experience
Designing backend logic and database structures
Integrating an LLM into a product workflow
Improving prompts, validation, or AI guardrails
Building APIs connecting product components
Configuring authentication and permissions
Debugging an issue discovered in production
Reviewing system performance and reliability
Discussing a new product idea with leadership
Rapidly prototyping a solution
Shipping a feature and monitoring how users interact with it
Improving architecture as product usage grows
In short: you own the engineering behind an AI-powered product from idea to production and continuously make it faster, smarter, safer, and more scalable.
Key Metrics for Success
Successful launch of a production-ready application
Speed and consistency of feature delivery
System stability and reliability
Application performance
Quality and usefulness of AI integrations
Effective handling of AI errors and edge cases
Reduction in bugs and recurring production issues
Scalability of application architecture
Maintainability and quality of the codebase
Ability to iterate rapidly based on real product feedback
Why This Role Stands Out
Build an AI-powered product from the ground up
End-to-end ownership across the full technology stack
Direct involvement in product and architecture decisions
Hands-on exposure to production LLM systems
Opportunity to build AI workflows and agents around real-world use cases
Work directly with leadership
High autonomy and execution ownership
Opportunity to take a product from MVP through production and scale
Fully remote environment
Interview Process
Initial Screening Call
Technical & Systems Design Interview
Practical Task – Product Build / AI Integration Scenario
Final Interview
Internal Review & Approval
Offer & Onboarding
Apply Now
If you:
Are a strong full-stack engineer
Have built and shipped real production products
Have hands-on experience integrating LLMs into applications
Understand AI hallucinations, edge cases, and guardrails
Can independently own frontend, backend, APIs, and infrastructure
Enjoy building products from zero rather than simply maintaining existing systems
We’d love to hear from you.
Important: Spark Hire Video Interview
As part of our application process, qualified candidates will be invited to complete a one-way video interview through Spark Hire .
This is your opportunity to introduce yourself and highlight your experience with full-stack product development, production AI/LLM integrations, system architecture, APIs, Supabase or similar backend platforms, security, and product ownership .
We recommend discussing a product you have personally built and shipped , including your specific technical contribution, the architecture you chose, how AI was integrated, how you handled hallucinations or failure states , and how you took the system from initial development into production.
Please complete your Spark Hire interview promptly after receiving the invitation. Candidates who do not complete the video interview may not move forward in the hiring process.