Senior AI Engineer, Agentic Data Enrichment

What You'll Do

• Own industry/category classification of businesses from heterogeneous signals (name, website, directory presence, reviews).
• Build and maintain discovery and verification systems for a business's real web presence - filtering aggregators, parked domains, brand collisions, and impersonators.
• Link individuals to businesses via public web evidence (e.g. confirming a named officer or employee genuinely works there).
• Develop risk/legitimacy scoring derived from web-presence signals, fed back into downstream underwriting.
• Build and evolve the shared agent infrastructure: provider-agnostic base agents, shared toolset registry (browser navigation, search, scraping, structured database lookups, scoring), eval harness, and instrumentation surface for token-and-tool tracing.
• Own model selection, agent design, prompt and tool engineering, eval methodology, and cost control across your enrichment surface.

Minimum Requirements

• Shipped LLM-driven agents to production - not notebooks, not demos. Real users, real cost, real failure modes, real on-call.
• Strong async Python including structured-data libraries, modern web frameworks, and relational databases.
• Experience across multiple frontier LLM providers and at least one agent framework, with deep knowledge of failure modes.
• Built or maintained eval methodology: curated golden datasets, scoring functions, labelling guidelines, regression diagnostics.
• Browser automation experience: headless browsers, anti-bot evasion, authenticated flows.
• Holds informed opinions on structured-output reliability - when to use JSON-schema mode vs. function calling vs. extractor-on-top-of-text.

What Sets You Apart

• Web scraping at scale: anti-bot evasion, residential proxies, request fingerprinting, authenticated flows, CDN defeats.
• Eval-framework experience (e.g., LangSmith, Braintrust, Evals, or custom).
• Entity resolution / record linkage / fuzzy matching at scale.
• Browser-automation experience at the devtools-protocol level.
• Built a tool registry or toolset abstraction over multiple LLM providers.
• Cost/latency optimization: response caching, semantic caching, model routing (cheap-first then escalate), thinking-budget tuning, prompt-cache hit-rate work.