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Enterprise AI Deployment

Full-Stack AI Product & Agent Engineer

Build useful AI products and agent experiences that connect strong software engineering with evaluation, safety and real user workflows.

Remote, Time-Zone Requirements ApplyFull-time or project basedReferral reward $250
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About this path

This role builds production applications in which language or multimodal models are one component of a reliable product. It includes frontend, backend, integrations, data, testing and observability, with sound judgment about when to use an agent, deterministic logic or human review.

You will build production applications where AI models are one component of a larger, reliable system, spanning frontend, backend, integrations and data. Part of the job is engineering judgment: knowing when an agent is the right tool, when deterministic logic is safer, and when a human still needs to be in the loop. This role suits engineers who care as much about reliability as capability.

What you would own

  • Frontend and backend development for AI-powered product features
  • Integration of language or multimodal models into real workflows
  • Testing, observability and evaluation for AI-driven functionality
  • Judgment calls on agent versus deterministic logic versus human review
  • Data handling and API integrations supporting the product
  • Iteration based on real user feedback and evaluation results

You are likely a strong match if

  • You have shipped production full-stack applications
  • You have integrated LLM or multimodal APIs into real products
  • You think about failure modes and evaluation, not just feature demos
  • You are comfortable owning a feature end to end
  • You know when NOT to use an agent for a given problem
  • You write tests and monitor what you ship

Helpful, not required

  • Experience with agent frameworks such as LangChain or similar
  • Experience with observability tools for LLM applications
  • Familiarity with vector databases or retrieval systems
  • Prior work in an AI-focused startup or product team

What success looks like

  • Features ship reliably and handle real-world edge cases
  • AI-driven functionality is measurable and monitored, not a black box
  • Users trust the product enough to rely on it daily
  • Technical decisions about agents versus deterministic logic hold up over time

Practical proof that helps

  • A link to a production application you built, or a detailed case study
  • Code samples or a portfolio showing full-stack and AI integration work
  • A description of a failure mode you anticipated and handled

What being in the network gives you

  • Remote-first work with clients across the United States, Canada and Latin America.
  • Human review of your profile, automation organizes information, people decide.
  • One profile considered across current and future opportunities.
  • Referral rewards when someone you refer directly is successfully placed.
  • Full control over availability, matching and your data at any time.

One profile, many opportunities

Applying here creates a single reusable profile. If this path is not the right fit, you remain eligible for other suitable opportunities across the network.

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