Hire Vector Database Experts | Nearshore Software Development

Vector databases are a new type of database designed to store, manage, and search high-dimensional vector embeddings, the foundation of modern AI applications like semantic search, recommendation engines, and Retrieval-Augmented Generation (RAG). You need an expert who can leverage a vector database to build intelligent, scalable AI features. Our vetting process, powered by Axiom Cortex™, finds engineers who are masters of this emerging field. We test their ability to work with leading vector databases (e.g., Pinecone, Weaviate, Milvus), to design efficient indexing strategies, and to integrate them into a production AI/ML pipeline.

Is your similarity search slow and unable to scale?

The Problem

Performing nearest-neighbor search on a large number of high-dimensional vectors using traditional methods is computationally expensive and slow, making it impossible to build real-time AI applications.

The TeamStation AI Solution

We vet for engineers who are experts in vector search. They must demonstrate the ability to use a vector database to perform approximate nearest neighbor (ANN) search at scale, providing low-latency results for even the most demanding AI applications.

Proof: Low-Latency, High-Throughput Vector Search

Are you struggling to build a reliable Retrieval-Augmented Generation (RAG) system?

The Problem

Building a RAG system that provides accurate, relevant, and up-to-date information to your LLM is a complex challenge. You need a reliable way to store and retrieve the right context for your prompts.

The TeamStation AI Solution

Our engineers are proficient in building RAG pipelines with vector databases. They are vetted on their ability to chunk and embed documents, store them in a vector database, and retrieve the most relevant context to augment LLM prompts, reducing hallucinations and improving the quality of your responses.

Proof: Robust and Accurate RAG Pipelines

Core Competencies We Validate

Vector database concepts (embeddings, ANN, indexing)

Leading vector database platforms (Pinecone, Weaviate, Milvus)

Data ingestion and embedding pipelines

Retrieval-Augmented Generation (RAG) architecture

Performance tuning and filtering

Our Technical Analysis

The Vector Database evaluation focuses on building modern AI applications. Candidates are required to design and build a RAG pipeline, demonstrating their understanding of the end-to-end process from document ingestion to context retrieval. A critical assessment is their ability to choose the right indexing strategy and to tune the performance of the vector search. We also test their knowledge of different embedding models and their trade-offs. Finally, we assess their experience in operating a vector database in a production environment and integrating it with LLM frameworks like LangChain or LlamaIndex.

Related Specializations

LLMs (Large Language Models)Machine Learning Python Data Science

Explore Our Platform

About TeamStation AI

Learn about our mission to redefine nearshore software development.

About Us

Nearshore vs. Offshore

Read our CTO's guide to making the right global talent decision.

Read the Playbook

Ready to Hire a Vector Databases Expert?

Stop searching, start building. We provide top-tier, vetted nearshore Vector Databases talent ready to integrate and deliver from day one.

Book a Call