Live opening · Posted 7 days ago

AI Application Engineer (Full Stack)

Revley · United States (Remote)
Linkedin Yes
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At a glance

The key details from the original listing.

Posted 7 days ago
CompanyRevley
LocationUnited States (Remote)
Work modeYes
SourceLinkedin
Listed7 days ago

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About the role

Description supplied by the original job listing.

About Revley
Revley is building the operating system for online stores—bringing commerce, payments, subscriptions, analytics, and business operations into one system.
We are hiring an AI Application Engineer who can build production AI systems and ship across the full stack. This is not a model-training or AI research role. You will work at the application layer, turning foundation models and company data into reliable product experiences inside Revley OS.
What You’ll Do
Build production AI features that understand user intent, retrieve the right business context, use tools, and complete multi-step workflows.
Design agent orchestration, tool and function calling, structured outputs, context management, memory, retrieval, and human-in-the-loop approval patterns.
Connect AI applications to Revley OS through secure APIs, MCP tools, internal services, and event-driven workflows.
Build backend services and customer-facing product surfaces across TypeScript, NestJS, Next.js, React, PostgreSQL, and AWS.
Design reliable integrations using OAuth, webhooks, queues, scheduled jobs, and third-party APIs.
Implement tenant isolation, scoped authorization, idempotency, audit logs, rate limits, and safe execution for AI-triggered actions.
Create evals, tracing, monitoring, and feedback loops for accuracy, tool selection, latency, reliability, and task completion.
Debug the full execution path—from model output and orchestration through APIs, databases, and user interfaces.
Work directly with product and engineering to turn ambiguous operating problems into scoped, production-ready systems.
What We’re Looking For
Experience shipping AI-powered applications, agents, copilots, or workflow automation into production.
Strong knowledge of the AI application layer: tool/function calling, structured outputs, retrieval, context engineering, orchestration, evals, and observability.
Strong full-stack engineering experience with TypeScript and modern web applications.
Experience building APIs, distributed workflows, data models, OAuth integrations, and webhook systems.
Strong understanding of production concerns including security, permissions, multi-tenancy, testing, failure recovery, and monitoring.
Ability to evaluate AI systems empirically rather than relying only on prompt iteration.
Product judgment, clear communication, and evidence of owning meaningful systems from design through production.
Helpful Experience
MCP or comparable tool-integration protocols.
NestJS, Next.js, React, PostgreSQL, AWS, Vercel, queues, and serverless systems.
Commerce, payments, subscriptions, fintech, or operational software.
Conversational interfaces, workflow engines, or approval-based automation.
Prompt-injection defense and capability-based authorization.
You do not need experience training foundation models. We care about your ability to use modern AI models to build secure, reliable, useful software.
Why Revley
Build AI that takes useful, authorized action—not just generates text.
Work on technically demanding problems across AI, commerce, payments, and business operations.
Own meaningful product and infrastructure surface area in a small engineering team.
Ship quickly, observe real usage, and help define the architecture of Revley OS.
How to Apply
Email mailto:careers@revley.io with the subject “Application: AI Application Engineer” and include your résumé or LinkedIn, relevant GitHub/portfolio links, and a short description of one AI application you shipped—including what you owned and how you measured reliability.
Revley is an equal opportunity employer. We consider qualified applicants without regard to any characteristic protected by applicable law. Reasonable accommodations are available throughout the application and interview process.
This keeps the internal product scope private while giving qualified candidates enough technical signal. I’ll treat this as the canonical version going forward.

Work arrangement
Yes

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