AI Engineer

What a AI Engineer does

Build and operate AI-enabled software by combining application engineering, data handling, evaluation and responsible delivery.

Responsibilities

• Translate a product use case into bounded expected behavior.
• Integrate model or retrieval services behind documented interfaces.
• Build evaluation cases for expected behavior, missing context and failure recovery.
• Monitor quality, latency, cost and safety signals after release.

Skills and practices

• Software engineering and API design
• Data preparation and retrieval
• Evaluation and human review
• Observability, security and release practice

Try this

1. Start with a small versioned evaluation set.
2. Change one system variable at a time.
3. Record known limitations and handoff conditions.

A practical progression

• Entry: build a small evaluated AI feature.
• Mid-level: own integration, data quality and diagnostics.
• Senior: design system boundaries and release practices.

Resume guidance

• Describe inputs, evaluation and known failure cases.
• Do not claim accuracy gains without reproducible measurements.

How do I begin?

Build a small API-backed feature with an authored evaluation set.

Does one score prove safety?

No. Combine task checks with human review and risk-specific tests.