The world is full of prompt tinkerers. You need someone who can fine-tune Llama with QLoRA on your data, stand up vLLM with continuous batching, build an eval suite that catches regressions, and cut your OpenAI bill in half without losing quality. That's an LLM engineer. We have 60+ of them.
$22/hr
Senior LLM engineer
5 days
Free fine-tune or eval PoC
40–70%
Typical inference cost cut
Tell us in 60 seconds — fine-tune, eval, inference, or cost. We'll match a senior LLM engineer in 24 hours.
Replies in 4 business hours
Six engagement patterns we run constantly — pick the one that matches the problem keeping you up at night.
QLoRA on Llama 3, Qwen, or Mistral with your domain data. Better-than-GPT-4 quality on narrow tasks at 1/20th the inference cost, deployable on your own GPUs.
Golden datasets, reference metrics, LLM-as-judge calibrated to human ratings, regression suite gating every PR. Quality you can put on a dashboard and defend in a board meeting.
Production inference on AWS g5/g6, GCP A3, or your data center. Continuous batching, paged attention, tensor parallelism, autoscaling, structured-output decoding.
Prompt caching, semantic response caching, model routing (small first / large on escalation), structured output, batching. Typical 40–70% bill reduction with no quality loss.
JSON Schema enforcement (OpenAI structured output, Anthropic tool use, Outlines, Instructor). 99.9% schema-valid output instead of regex-and-pray.
Prompt injection defense, PII redaction, jailbreak resistance testing, content moderation, allow/deny routing. Required before any customer-facing deployment.
A model-agnostic team. We deploy what fits your latency, cost, compliance, and quality bar — not what we're used to.
We work in measurable iterations. If we can't show numbers in the first week, you walk away — no invoice.
We map your task, current quality, latency, cost, and constraints. Decide: prompt, fine-tune, or both. Pick base model and eval metrics.
Eval harness + baseline run + first improvement (better prompt, smaller model, or LoRA). You see real metrics on real data by Friday.
Fine-tune, deploy on vLLM, instrument observability, build the regression suite, and tune until target metrics hit.
Weekly eval re-runs, cost dashboards, model-upgrade testing on your golden set, and quarterly retraining when needed.
Three engagement models. Free PoC because metrics convince more than slide decks.
Eval + first improvement
Free
Baseline eval, one improvement (prompt, smaller model, or quick LoRA), real numbers reported. No contract.
6–12 weeks
$25K – $120K
Full fine-tune + serving + eval + observability + handoff. Fixed scope, fixed price, defined success metrics.
Monthly
$22 – $48/hr
Senior or principal-level engineer embedded in your team. Best for evolving R&D with publications-grade work.
We pick engineers the way you should pick LLMs: by measurable output, not by marketing.
Every engagement starts with eval. Every change ships with before/after numbers.
Equally fluent with Llama on vLLM and GPT-4o on the API. We pick based on your constraints.
Data prep, training, serving, observability, and ops — one team, one accountability.
We measure cost per request from day one and ship optimization as a feature, not an afterthought.
An LLM engineer specializes in the unique stack around large language models — prompt design, structured outputs, function calling, fine-tuning (LoRA, QLoRA, DPO), inference serving (vLLM, TGI, SGLang), eval frameworks, and the cost / latency tradeoffs that don’t exist in classical ML. Data scientists model patterns. ML engineers ship models. LLM engineers ship prompts, models, eval suites, and inference infrastructure as one system.
Almost always start with prompting and few-shot. Fine-tune only when you have a clear reason: (1) a narrow domain where prompt context is too long or too expensive, (2) a stylistic or format requirement that prompts can’t reliably enforce, (3) latency-critical paths where a small fine-tuned model beats a large hosted one, or (4) data that legally cannot leave your VPC. Our LLM engineers will tell you honestly which case applies.
All four, picked per use case. LoRA / QLoRA on Llama, Qwen, or Mistral for most domain adaptation work — cheap, fast, mergeable. Full fine-tuning rarely, only when LoRA caps out. DPO and ORPO for preference alignment when you have human feedback data. We use Axolotl, Unsloth, and TRL for training, and we report eval scores against the base model so you see the actual lift.
vLLM is our default for high-throughput inference — paged attention, continuous batching, and tensor parallelism out of the box. SGLang for complex prompting patterns. TGI for tighter HuggingFace integration. We deploy on AWS g5/g6 instances, GCP A3, RunPod, or your on-prem GPUs, with autoscaling, request batching, and Prometheus metrics. For low-volume needs, we proxy through Together, Fireworks, or Groq instead of running the GPU ourselves.
Three layers. (1) Reference-based evals against a golden dataset — exact match, ROUGE, BLEU where appropriate, plus task-specific metrics. (2) LLM-as-judge with a stronger model rating outputs on faithfulness, helpfulness, format compliance — calibrated against human ratings on a sample. (3) Production logging and pairwise A/B between model versions. We use OpenAI Evals, LM Eval Harness, Promptfoo, LangSmith, or Braintrust depending on your stack.
Yes — typically 40–70% in the first month. Tactics: prompt caching, response caching with semantic similarity, smaller-model routing for simple requests, structured output to cut tokens, batching for non-real-time work, and hybrid deployments where the cheap path goes to an open model on Groq and complex requests escalate. We measure before and after on the same eval suite so quality is held constant.
Free 5-day PoC for fine-tuning or eval projects. Production builds run $25K–$120K depending on scope (data preparation, training compute, eval rigour, inference deployment). Dedicated LLM engineer: $22–$36/hr for senior, $30–$48/hr for principal-level researchers with publications.
Tell us the task. We'll set up the eval, run a real experiment, and report measured gains by Friday — no contract, no fee.