Applied AI Engineer

Responsibilities

• Take an open problem end-to-end — from research and prototyping through production, killing what doesn't work before it becomes someone's roadmap
• Design and ship agent-powered features — root-cause analysis, incident triage, monitor generation — and integrate them into the platform with our engineering team
• Build the eval infrastructure that makes those features safe to change: golden datasets, regression suites, offline and online scoring, and the judgment calls about what "good" means
• Own retrieval and context pipelines over customer metadata, lineage, and query history, and instrument agent behavior in production — traces, failure taxonomies, cost and latency budgets — to close the loop on quality
• Partner with data science on detection quality and experiment design, and with PM on what an agent should do versus what it merely can do
• Set the technical bar for how we build with LLMs — patterns, guardrails, and the internal tooling other engineers reuse

Languages