Sobre esta vaga de AI Product Engineer (Full Stack) na Pavago
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.
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