Ai-Native Full Stack Engineer, Typescript — Profasee Ultra, Gobernador Gálvez

Profasee builds AI employees for Amazon brands.
Our platform, Profasee Ultra, runs a coordinated team of named AI agents that handle PPC, dynamic pricing, demand planning, FBA reimbursements, supply chain ops, catalog audits, and listing health.
Sellers replace their agency, their tool stack, and their in-house hires with one system.
We have around 120 live customers, a 90-person waitlist, and we're growing fast.
Founded by Chad Rubin, who previously built and sold Skubana and Prosper Show.
We are a small, product-focused team.
We move fast.
We expect every engineer to use the best available tools, including AI, to ship excellent work.
The role
We are looking for an AI-native full stack engineer who is strong in TypeScript and knows how to build real production software with the help of AI tools.
This is not a wait-for-a-ticket role.
You will take ambiguous product goals, use AI tools like Claude Code, Codex, Cursor, or similar systems to move faster, and apply strong engineering judgment when the model gets things wrong.
You should be comfortable working across the stack: frontend, backend, APIs, workflows, data models, integrations, QA, debugging, and production stability.
What you will do
Build and improve features across the Profasee Ultra TypeScript application
Work on AI agent workflows that help Amazon brands make decisions and execute work
Build frontend experiences for our agent UI
Build backend services, APIs, workflow logic, and integrations
Improve platform stability, observability, QA coverage, and production reliability
Use AI coding tools every day to accelerate development, investigate issues, write tests, refactor, and ship faster
Review AI-generated code critically and make sure it is correct, maintainable, secure, and production-ready
Translate business goals into working software without needing every detail spelled out
What you bring
Strong professional experience building production software with TypeScript
Experience across the full stack: frontend, backend, APIs, databases, and production debugging
Strong React and modern frontend experience
Ability to reason through complex systems and understand upstream and downstream impact before making changes
Hands‐on experience with AI coding tools such as Claude Code, Codex, ChatGPT, Cursor, or Copilot
A clear point of view on how to use AI effectively in software development
Strong debugging instincts and the ability to verify that something actually works, not just that the code compiles
Comfort in a fast‐paced, high‐ownership startup environment
Strong time‐zone overlap with the U.S.
Nice to have
Experience with agentic AI systems, LLM workflows, tool‐calling, or autonomous agents
Experience with Amazon, e‐commerce, marketplaces, advertising, pricing, inventory, or seller operations
Experience with workflow systems, queues, background jobs, or event‐driven architecture
Experience with production observability, logging, monitoring, and incident debugging
Experience in small teams where engineers own features end to end
React 18, TypeScript, Node, Prisma, PostgreSQL on Railway, shadcn/ui, Tailwind.
Next.js on Vercel for the marketing site.
pg-boss for job queues.
Multi-provider LLM routing across Anthropic, OpenAI, Gemini, and OpenRouter.
Who this role is not for
This is the wrong role if you only want to do machine learning research, model training, or prompt experiments without owning production application code.
It is also the wrong role if you want a narrow frontend‐only or backend‐only lane.
We need someone who can work across the product and use AI to increase their leverage.
How we work
We use AI aggressively, but we do not blindly trust AI-generated code.
The right person knows how to prompt well, inspect outputs carefully, test thoroughly, and keep improving the system.
Use AI to move faster while still thinking like an engineer.
Our process includes a practical AI-assisted build exercise.
We expect you to use AI.
We want to see how you use it.
You will build a small TypeScript application using your preferred AI tools.
We evaluate:
What you built
How well it works
How you structured the code
How you used AI
The prompts and workflow you used
How you verified correctness
How you handled bugs, edge cases, and tradeoffs
Whether you understand the code you shipped
#J-*****-Ljbffr