Hire LLM & Generative AI Experts | Nearshore Software Development

Large Language Models (LLMs) are a transformative new technology that is enabling a new generation of intelligent applications. You need an expert who can navigate the rapidly evolving landscape of LLMs and apply them to solve real business problems. Our vetting process, powered by Axiom Cortex™, finds engineers who are at the forefront of this new field. We test their ability to work with models from OpenAI, Anthropic, and open-source providers, and to build complex applications using frameworks like LangChain and LlamaIndex.

Are your LLM-powered features unreliable and prone to 'hallucinations'?

The Problem

LLMs are powerful, but they can also be unpredictable and make things up. Building a reliable application on top of them requires a deep understanding of their limitations and how to mitigate them.

The TeamStation AI Solution

We vet for engineers who are experts in building reliable LLM applications. They must demonstrate the ability to use techniques like Retrieval-Augmented Generation (RAG) to ground the model in your own data, reducing hallucinations and improving accuracy.

Proof: Reliable and Factual LLM-Powered Applications

Are you struggling to move your LLM prototypes into production?

The Problem

Building a production-ready LLM application involves more than just calling an API. You need to manage prompts, handle context, evaluate performance, and deploy your application in a scalable and cost-effective way.

The TeamStation AI Solution

Our engineers are proficient in the emerging field of LLMOps. They are vetted on their ability to use tools and best practices to build, deploy, and monitor LLM applications at scale, ensuring they are reliable, performant, and cost-effective.

Proof: Production-Ready LLM Applications

Core Competencies We Validate

LLM fundamentals and model selection (OpenAI, Anthropic, open-source)

Prompt engineering and optimization

Retrieval-Augmented Generation (RAG) with vector databases

Fine-tuning and model customization

LLMOps (deployment, monitoring, evaluation)

Our Technical Analysis

The LLM evaluation focuses on the practical application of large language models. Candidates are required to build a complete LLM-powered application, such as a question-answering system over a set of documents. A critical assessment is their ability to implement a robust RAG pipeline, including document chunking, embedding, and retrieval. We also test their prompt engineering skills and their ability to fine-tune a model for a specific task. Finally, we assess their understanding of the operational challenges of running LLM applications in production.

Related Specializations

Python Data Science Machine Learning Vector Databases

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