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Enterprise AI Deployment

Data & Enterprise Integration Engineer, AI Systems

Connect AI applications to the trusted data, APIs, permissions and enterprise systems they need to operate safely in production.

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

This role builds pipelines, connectors, schemas, APIs, identity controls and observability. It is an integration and data-engineering role first, with applied AI context.

You will build the data pipelines, connectors and identity controls that let AI applications operate safely against real enterprise systems. This is fundamentally a data and integration engineering role, with applied AI as the context rather than the core skill. It suits engineers who take permissions, schemas and observability seriously.

What you would own

  • Data pipeline design and implementation for AI application needs
  • API connectors and integrations with enterprise systems
  • Schema design and data-quality controls
  • Identity and permissions controls for secure data access
  • Observability and monitoring for data flows feeding AI systems
  • Documentation of integration architecture for other engineers

You are likely a strong match if

  • You have built data pipelines or integrations in a production environment
  • You understand identity, permissions and access control patterns
  • You care about schema design and data quality
  • You can build observability into data flows, not just the application layer
  • You have some familiarity with how AI applications consume data
  • You document integration architecture clearly

Helpful, not required

  • Experience with ETL or data-pipeline tools
  • Experience with enterprise identity systems such as SSO or SCIM
  • Familiarity with vector databases or retrieval-augmented generation
  • Experience with cloud data platforms

What success looks like

  • AI applications reliably access accurate, permissioned data
  • Data pipelines are observable and failures are caught quickly
  • Integration architecture is documented well enough to hand off
  • Security and access controls hold up under review

Practical proof that helps

  • A description of a data pipeline or integration you built in production
  • Architecture diagrams from past integration work, sanitized as needed
  • Examples of how you handled permissions or identity in a data system

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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