Hire for LLMs Mastery
Building with Large Language Models is a new frontier of systems engineering. A compelling demo is easy; a production-ready, reliable, and cost-effective LLM application is brutally hard. You need an **LLM Engineer** who masters the entire stack, from data pipelines for Retrieval-Augmented Generation (RAG) to the complex trade-offs of inference optimization. Our vetting identifies engineers who can tame hallucinations, build robust evaluation systems, and turn the promise of generative AI into a hardened, valuable asset.
Sound Familiar?
Common problems we solve by providing true LLMs experts.
Unreliable, Hallucinating Models
The Problem
Your LLM-powered application frequently makes up facts, provides dangerously incorrect information, or goes off-topic, destroying user trust and creating significant business risk.
The TeamStation AI Solution
An LLM Engineer architects and implements a robust Retrieval-Augmented Generation (RAG) pipeline. They use vector databases and sophisticated retrieval strategies to ground the model in your specific, factual data, dramatically reducing hallucinations and ensuring answers are relevant and accurate.
Proof: Reduce model hallucinations by >95% by grounding responses in verifiable data.
Skyrocketing and Unpredictable Inference Costs
The Problem
Your proof-of-concept with a proprietary API was impressive, but the cost-per-token is commercially unviable at scale. You have no clear path to making the unit economics work.
The TeamStation AI Solution
Our LLM experts are masters of cost optimization. They can fine-tune smaller, open-source models, implement efficient caching strategies, and use tools like vLLM to optimize inference, bringing your operational costs in line with your business model.
Proof: Decrease the cost-per-inference by 50-80% through model and infrastructure optimization.
Impossible to Measure or Guarantee Quality
The Problem
You have no objective way to measure if your LLM application is getting better or worse. You are flying blind, unable to evaluate different prompts, models, or RAG strategies in a systematic way.
The TeamStation AI Solution
A TeamStation LLM Engineer builds a rigorous evaluation framework. Using tools like Ragas, they create automated test suites to measure metrics like faithfulness, answer relevancy, and context recall, allowing you to iterate and improve with confidence.
Proof: Establish a quantitative, automated evaluation pipeline for 100% of LLM development cycles.