AI Engineer II - Trust & Explainability (AI Platform)

Responsibilities

• In the first few months, a successful hire has shipped tracing that follows a full agent workflow end to end, readable by an engineer who did not write it, and has contributed working components to the platform's evaluation harness. Over the first year, success looks like this: a first version of the customer-facing…
• Multi-Agent Tracing & Explainability
• Build tracing across the platform's gateway, orchestration, memory, and tool layers, following defined designs from the team lead and architects
• Build the correlation that links actions across agents in a single multi-agent workflow, including handoffs, parallel branches, and retries
• Build the developer-facing trace view that makes a full agent conversation readable to someone who did not write the agent
• Build the explanation layer that turns trace data into a human-readable account of why an agent did what it did
• Customer-Facing Trust
• Build the platform primitives product teams surface to end users: explanation records, confidence and provenance metadata, and "what the agent relied on" summaries
• Partner with product engineers on the Document Request Agent and the MLM agents to land those primitives in real products
• Iterate on the explanation format based on feedback from product teams and customer-facing staff

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