# Senior AI Engineer, Agentic Data Enrichment

**Company:** [Baselayer](https://hotfix.jobs/companies/baselayer)
**Location:** San Francisco, CA
**Role:** ML Engineering
**Salary:** $230k – $340k/yr
**Experience:** 5+ years
**Skills:** Llm Agents, Async Python, Browser Automation, Web Scraping, Entity Resolution, Prompt Engineering, Eval Methodology, Langsmith, Braintrust, Fuzzy Matching
**Posted:** 2026-07-01

> Senior AI Engineer owning end-to-end LLM-driven agent systems for business data enrichment, web presence verification, entity linking, and risk scoring at Baselayer. Requires production LLM agents experience, async Python, browser automation, and eval frameworks.

## Job Description

## 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.

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