LLM Engineer Staffing
Most candidates who list “LLM” on a resume have wired up an OpenAI API key and called it done. We place engineers who have shipped retrieval, fine-tuned open weights, owned an eval harness, and survived a production hallucination at 2am. Vetted by recruiters who’ve staffed AI specialties since the transformer paper dropped.
LLM engineering is a sharp specialty inside our broader AI/ML engineer staffing practice. Adjacent searches often flex into NLP engineer staffing and Python developer staffing.
KORE1 places LLM engineers who have shipped production language-model systems, with an average 17-day time-to-hire and a 92% 12-month retention rate across our AI placements.
Last updated: June 1, 2026
The Resume Says “LLM.” The Engineer Has Shipped One Three Times.
Every team building with language models in 2026 is hiring against the same noisy pool. Most candidates have called an API. A smaller number have written a RAG demo over a PDF. A much smaller number have actually owned an LLM system in production. They’ve debugged a stale chunk strategy at 1am. They’ve watched evals slowly drift after a model version bump. They’ve explained to a CFO why the OpenAI bill tripled in a week. The annual State of AI Report tracks how fast organizational adoption has scaled while the bench of people who can ship and own GenAI in production has barely moved.
That gap is where wrong hires get made. It’s also where we screen. We’ve placed LLM engineers since teams were still arguing about whether to host their own weights. The Stack Overflow 2024 Developer Survey AI section shows most working developers have only used AI tools, not built or fine-tuned them. LLM staffing sits as a deep specialty inside our IT staffing services practice, so the recruiter who calls you understands the role, not just the keywords.
Request LLM Talent →“Nearly 70% of generative AI projects stalled in 2025 due to talent gaps and engineering complexity.”
— Gartner, 2025 GenAI Outlook
Flexible Ways to Bring on LLM Talent.
Some teams need a contract LLM engineer for an eight-week RAG rebuild. Some need a permanent platform lead who’ll set the eval bar for the whole company. We support every model, and we’ll tell you up front when the one you asked for isn’t the right one.
Contract
Drop-in expertise for a defined build. RAG pipeline, eval harness, fine-tune sprint, or vendor migration. Fast onboarding, no long-term commitment.
Contract-to-Hire
Run the engineer against your actual stack and prompts for 90 days before converting. Useful when the role is new and scope is still moving.
Direct Hire
Permanent seat for a senior or lead LLM engineer who’ll own architecture, eval strategy, and mentor the rest of the team.
Project Consulting
Scoped engagement. Vector store rollout, eval framework build, prompt-injection hardening, or a model-vendor migration on a deadline.
LLM Roles We Place.
“LLM engineer” is four jobs in a trench coat. The integrator wires APIs into product. The platform engineer owns inference, gateways, and cost. The applied scientist runs fine-tunes and evals. The research-leaning hire pushes new architectures or distillations. We screen for the lane, not the title on LinkedIn.
Roles we’ve placed
- LLM Engineer (integrator, platform, applied)
- GenAI Engineer / Generative AI Engineer
- RAG / Retrieval Engineer
- Prompt Engineer / Prompt Architect
- LLM Platform Engineer (gateways, routing, observability)
- Applied Scientist, Language Models
- LLM Fine-Tuning Engineer (LoRA, QLoRA, SFT, DPO)
- AI Agent Engineer (LangGraph, AutoGen, CrewAI)
- LLM Safety / Red Team Engineer
- LLM Evaluation Engineer (RAGAS, OpenAI Evals, custom harnesses)
- Conversational AI Engineer
Common stacks we screen against: OpenAI, Anthropic, Google Gemini, AWS Bedrock, Vertex AI, Azure OpenAI, open-weight families like Llama 3, Mistral, Mixtral, and Qwen, plus the production layer of vLLM, TGI, Triton, Ray, LangChain, LangGraph, LlamaIndex, and the vector stores Pinecone, Weaviate, pgvector, and Qdrant. For background on the role family, the BLS Occupational Outlook Handbook tracks Computer and Information Research Scientists as the federal category that covers applied LLM work.
How We Hire LLM Engineers That Move the Needle.
Scope the role honestly
We get on a call and pin down the actual LLM work. Integrator or platform. Hosted API or open weights. Latency budget, token economics, eval bar, and what “good” looks like on day 90.
Source and technically vet
Our recruiters know what shipped LLM work looks like. We screen for retrieval rigor, eval design, prompt versioning, guardrails, and the failure stories. Shortlist usually lands inside two weeks.
Stay close after start date
We check in at 30, 60, and 90 days with both the engineer and the hiring manager. If something’s off, we want to know early. That’s how we hit 92% retention.
What “Vetted” Means When It’s LLM Work.
Every candidate we put in front of you has been through a technical screen run by a recruiter who can tell the difference between someone who’s wrapped an API and someone who’s owned an LLM in production. We don’t farm screens out. We ask about chunking strategy, retrieval evaluation, prompt versioning, observability, jailbreak hardening, and the boring infrastructure work that decides whether a model survives contact with real users.
“Three of our last LLM placements landed at a fintech, a HealthTech, and an enterprise SaaS. All three closed inside three weeks because we’d already pipelined the talent before the req opened. Two of them were senior platform hires, the kind most agencies can’t even screen.”
— Devin Hornick, Partner at KORE1
- Real fine-tuning experience with LoRA, QLoRA, SFT, or DPO, not just notebooks
- Eval frameworks for hallucination, retrieval quality, drift, and bias
- Production deployment with rollback, canary, and model-version pinning
- Cost-per-query instincts and gateway-level routing experience
- Compliance fluency for HIPAA, SOC2, PCI, and PII redaction at prompt and output layer
If you’re still scoping comp bands, our prompt engineer salary guide is a useful anchor for adjacent roles, and our 2026 guide to hiring LLM engineers breaks down the three sub-roles, the comp bands, and the five resume-padder tells we screen out before you ever see a profile.
Common Questions
How quickly can KORE1 deliver vetted LLM engineers?
Our average time-to-hire for LLM engineers is 17 days, and most senior LLM platform and applied scientist roles close in three to four weeks.
We hold an active pipeline of pre-screened LLM talent across OpenAI, Anthropic, AWS Bedrock, Vertex AI, Azure OpenAI, and the major open-weight stacks. When you open a req, we’re not starting from a job board. For urgent contract needs we’ve placed an LLM integrator inside five business days. For senior platform or fine-tuning leads we usually need three to four weeks because the bench is genuinely thin.
What does an LLM engineer actually cost in 2026?
LLM engineers in 2026 land at $175K to $230K base for mid-level and $260K to $380K for senior, with applied scientists and platform leads pushing well above that in major tech metros.
Total comp varies a lot by stage. A Series B startup hiring its first LLM engineer pays a different number than a hyperscaler hiring its fifteenth. Contract rates run $145 to $245 per hour W-2 depending on stack depth, fine-tuning history, and clearance. We share live market data when we scope the role with you, not after.
Is an LLM engineer different from an NLP engineer or an AI/ML engineer?
LLM engineers specialize in large language models, retrieval, fine-tuning, agent workflows, and production GenAI systems, while NLP engineers cover the broader language-data spectrum and AI/ML engineers cover vision, recsys, tabular, and classical models too.
There’s real overlap. Most senior LLM engineers can hold a credible NLP conversation. The reverse isn’t always true. Generalist ML engineers usually need ramp time on retrieval, eval, and prompt rigor. If you’re scoping a broader role, our NLP engineer staffing and machine learning engineer staffing pages cover the adjacent specialties.
What stacks and tools do you screen LLM candidates for?
We screen across the modern LLM stack, including OpenAI, Anthropic, Google Gemini, AWS Bedrock, Vertex AI, Azure OpenAI, Llama 3, Mistral, vLLM, LangChain, LangGraph, LlamaIndex, plus vector stores like Pinecone, Weaviate, pgvector, and Qdrant.
We also screen for the unglamorous parts that decide whether a system survives contact with users. That means eval harness design, prompt versioning, retrieval ranking quality, tokenization edge cases, gateway routing, observability for drift and hallucination, and red-team patterns for prompt injection. If your team runs a more specialized stack, we calibrate the screen before sending anyone.
Can KORE1 staff LLM engineers for regulated industries?
Yes. We regularly place LLM engineers into healthcare, fintech, public-sector, and legal environments where HIPAA, SOC2, PCI, and PII redaction are non-negotiable.
For these placements we pre-screen for prior regulated-industry experience and for the specific compliance patterns the client cares about. That includes VPC-isolated inference, on-prem deployments of open-weight models, prompt and output logging policies, audit trails for retrieval sources, and red-team review workflows. If the role is more clinical than language-focused, our healthcare IT practice is a separate dedicated path.
Do you place remote LLM engineers across the United States?
Yes. We place LLM engineers remotely across 30+ U.S. metros, with strong density in San Francisco, the Bellevue-Redmond corridor, Austin, Boston, and the Irvine and Newport Beach area where our HQ sits.
Most LLM roles in 2026 are remote-first or hybrid. We honor time-zone and onsite preferences. If you want regional focus, we tighten the funnel. If you want the strongest available candidate regardless of zip code, we widen it. Fully on-site searches still happen, mostly for defense, regulated healthcare, and finance clients with secure-environment requirements.
Do you place LLM engineers at startups, or only enterprise teams?
Both. Roughly half of our LLM placements over the past year landed at Series A through Series C startups, the other half at mid-market and enterprise teams.
Startups usually need a builder who can stand up the function alone and own the roadmap. Enterprises usually need depth in a specific area like retrieval quality, agent orchestration, or eval rigor. Our pipeline is segmented by stage and specialization, so we don’t waste your time sending the wrong profile.
Ready to Hire an LLM Engineer Who Has Actually Shipped?
The pool of engineers with real production LLM experience is small and the wrong hire sets a roadmap back a quarter. We’ve spent two decades placing technical specialists and the last several years getting deep on language-model work specifically. Tell us what you’re building and we’ll bring you the people who can build it.