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LLM Engineer — Enterprise RAG | Glean | Palo Alto · Ganloss
LLM Engineer — enterprise RAG
LLM Engineer — enterprise RAG
Glean
Este anuncio está pensado para talento nativo en IA : habilidades y herramientas claras para saber si encajas antes de aplicar — y para reducir descartes por desajuste.
3 habilidades 1 herramienta
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Modalidad Hybrid
Ubicación 2600 El Camino Real, Palo Alto, CA 94306, United States · Hybrid
Compensación $180k–$240k + equity Nivel de experiencia Beneficios Equity, hybrid, comprehensive medical. Cómo aplicar Enterprise RAG or connector integration story.
Publicada 12 jul 2026 Glean is hiring a Senior LLM Engineer — enterprise RAG on the Enterprise search team (Hybrid · Palo Alto, United States). This is a hands-on role in LLM & generative AI —not a generic “AI enthusiast” post.
You will own LLM features, retrieval, agents, and evaluation end to end: problem framing, offline evaluation, safe rollout, and iteration from production signals. Success is measured with clear metrics (quality, latency, cost, or business KPIs)—not slide decks alone.
Glean connects enterprise knowledge with LLM search and agents used by Fortune 500 teams.
LLM Engineer — enterprise RAG (Palo Alto / hybrid): connectors, ACL-aware retrieval, and quality evals.
What you will do
Deliver production-grade enterprise RAG with measurable quality, latency, and cost targets. Build offline evals and online monitoring before scaling to full traffic. Collaborate with product, design, legal, or domain experts on safe rollout. Write clear RFCs, runbooks, and postmortems for cross-team learning. What we look for
Multiple shipped ML/LLM features—not coursework-only portfolios. Strong Python, SQL, and experimentation discipline. Clear communication for remote or hybrid stakeholders. Work authorization for the listed country. Python
Production Python for data pipelines, model code, APIs, and automated tests—not notebook-only workflows.
LangChain
Orchestrating LLM chains, tools, and retrieval with observable steps and failure handling.
Vector DBs
Embedding storage, hybrid search, and freshness strategies for RAG and semantic retrieval.
✓ You can show evidence in your application: Enterprise RAG or connector integration story. ✓ Several of these show up in recent shipped work—not only on your CV: Python, LangChain, Vector DBs, and Docker. ✓ You have owned LLM features, retrieval, agents, and evaluation in production: debugging live issues, running postmortems, and iterating from real user or business signals. ✓ You are comfortable with the hybrid rhythm (Hybrid · Palo Alto, United States)—onsite collaboration when it matters, deep work when remote. ✓ You explain trade-offs (quality, cost, latency, safety) in plain language—with numbers or examples, not buzzwords. ✓ You read the full listing (comp: $180k–$240k + equity, benefits in At a glance) and your expectations align. Lo que pide la empresa
Enterprise RAG or connector integration story.
Inicia sesión con cuenta candidato para aplicar
Regístrate, añade titular y bio; luego podrás adjuntar enlace o PDF de CV en Mi cuenta antes de aplicar. Así las candidaturas quedan ligadas a tu perfil.
Sugerencias
Talento IA que podría interesarte Perfiles ordenados por solape con habilidades y herramientas de este rol — útil si contratas equipo o comparas candidatos.
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Título
LLM Engineer — enterprise RAG
Aplicar Pensado para equipos que contratan personas que trabajan con IA, no solo alrededor.
Los candidatos ven habilidades y herramientas al inicio; recibes candidaturas estructuradas en un solo panel.
Llega a profesionales de IA que buscan roles con expectativas basadas en pruebas.
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