Employers · LLM hiring
Hire LLM engineers — vetted for production RAG & agents
Target engineers who ship retrieval, tool use, eval harnesses, and low-latency inference—not slide-deck “AI strategy.” Search proof-first profiles or publish a role that spells out your stack.
Skills we help you screen for
Use this checklist when writing your job post or filtering talent search.
- Python & production backend for LLM APIs
- RAG pipelines, chunking, and vector databases
- LangChain, LlamaIndex, or custom agent frameworks
- LLM evaluation, regression gates, and observability
- Prompt routing, caching, and cost/latency tradeoffs
- MLOps: model registry, canaries, and rollback playbooks
- Security: prompt injection, tool sandboxing, data boundaries
- Seniority from mid-level to staff/platform
US hiring teams
Many LLM engineers on Ganloss work US-friendly hours. List timezone overlap, W2 vs contractor expectations, and salary bands in your post—US employers see clearer self-selection and higher-quality applicants.
Colecciones
Hubs por intención
Páginas con filtros preaplicados en el tablero público—remoto e híbrido, ML y junior ML, NLP/LLM.
Colecciones
Hubs de talento por intención
Páginas que abren el directorio con búsquedas preajustadas—ML, junior ML, NLP/LLM, MLOps y visión por computador.
Why a dedicated LLM hiring page
Generic job boards collapse “LLM engineer” into buzzwords. Ganloss keeps stack vocabulary aligned between profiles and posts so you spend less time decoding résumés and more time on system design interviews.
Filter by LLM stack
Search profiles mentioning RAG, agents, eval tooling, and the vector stores you actually use.
Post roles candidates parse
Structured fields for workplace, location, employment type, and tools—plus a full description of production constraints.
Structured applications
Applicants arrive with profile context and optional CV material tied to verified accounts.
Export when volume grows
Pull applicant or inquiry data into spreadsheets or ATS workflows without manual copy-paste.
Related resources
- Hire AI talent (broader hub) — all AI/ML roles beyond LLM-only hiring.
- AI hiring guide — job posts, screening, and stack alignment.
- Open AI/LLM roles — see how candidates experience listings.
- How to hire LLM engineers — playbook without guesswork.
- LLM evaluation scorecard — 1–4 dimensions for panel debriefs.
- Prompt interview rubric — live exercise + FAQ for prompt roles.
FAQ — hiring LLM engineers
Timeline depends on seniority and stack niche. Many teams start with talent search the same week they publish a role; structured posts reduce mismatched applications so screening moves faster.
Yes—profiles span US, UK, EU, and remote-friendly time zones. Filter by location and tools, and state timezone overlap in your job post.
LLM engineers focus on retrieval, agents, inference APIs, evals, and product integration around foundation models. ML engineers often own training pipelines, feature stores, and classical modeling—both may overlap on MLOps.
Many listings support full-time, contract, or freelance paths. State employment type and rate/salary expectations in the post so candidates self-select.
Name frameworks (LangChain, vLLM, etc.), vector stores, eval practices, latency/cost constraints, and whether the role is research-leaning or production-first.
No. Ganloss targets full product roles—LLM platform, applied AI, and agent features—not short annotation tasks.