Vector Database Specialist (Pinecone / Milvus / Weaviate)

Zoho; United States; Remote; USD 83,000 - 165,000; Part time

Job summary

Fyerx seeks an experienced Vector Database Specialist to design, configure, and optimize the storage infrastructure powering production-grade GenAI and semantic search applications.

The ideal candidate will architect highly scalable vector indexes, write high-throughput embedding ingestion pipelines, configure real-time hybrid search query spaces, and maintain low-latency vector infrastructure handling millions of embeddings.

Qualifications

• 2+ years actively scaling high-dimensional vector databases.
• Experience with vector index design and embeddings pipelines.
• Familiarity with hybrid search architectures.
• 4–8 years in data engineering or backend development.
• Certification in Vector DB or cloud data engineer preferred.

Responsibilities

• Design, deploy, and govern production vector databases (Pinecone, Milvus, Weaviate, Qdrant, or pgvector) to manage complex long-term memory structures for LLM applications.
• Build high-performance embedding ingestion pipelines, managing data chunking strategies, overlap controls, metadata schema extractions, and real-time upsert queues.
• Optimize high-dimensional vector search spaces, fine-tuning ANN graph parameters, HNSW metrics, IVF index lists, and scalar quantization bounds.
• Configure advanced hybrid search architectures, engineering unified retrieval flows combining vector lookups with BM25."
• Implement strict metadata filtering schemas, constructing optimized filter patterns to speed up context retrieval times and enforce dynamic domain isolation safety parameters.
• Monitor cluster metrics and resource optimization loops, tracking vector pod memory, index reconstruction latencies, QPS thresholds, and compute costs.
• Collaborate with AI and Data Engineering squads to evaluate text embedding models (e.g., OpenAI, Cohere, Hugging Face) and map vector sizing to downstream runtimes.

Skills

Python

Advanced SQL

Vector databases

ANN / high-dimensional search

Data engineering

Performance tuning

Metadata filtering

Tools

Pinecone

Milvus

Weaviate

Qdrant

pgvector

Spark

dbt

OpenAI embeddings

Cloud auto-scaling

Job description

Vector Database Specialist (Pinecone / Milvus / Weaviate)

Vector Database Specialist (Pinecone / Milvus / Weaviate)

• Employment Type: Contract
  
• Work Mode: Remote
  
• Location: Offshore
  
• Total Experience Required: 4 to 8 years
  
• Relevant Experience Required: 2+ years of dedicated data engineering experience specializing in vector database administration, architectural index design, and high-dimensional semantic search scaling
  
• Mandatory Certification: Developer or Administrator certification from a major Vector DB provider (e.g., Pinecone Certified Developer, Milvus Professional) or a major cloud provider Data Engineering Specialty
  

Job Summary

We are seeking an experienced Vector Database Specialist to design, configure, and optimize the storage infrastructure powering our production-grade GenAI and semantic search applications. The ideal candidate will architect highly scalable vector indexes, write high-throughput embedding ingestion pipelines, configure real-time hybrid search query spaces, and maintain low-latency vector infrastructure handling millions of high-dimensional embeddings.

Key Responsibilities

• Design, deploy, and govern production vector databases (e.g., Pinecone , Milvus , Weaviate , Qdrant , or pgvector) to manage complex long-term memory structures for LLM applications.
  
• Build high-performance embedding ingestion pipelines, managing data chunking strategies, overlap controls, metadata schema extractions, and real-time upsert queues.
  
• Optimize high-dimensional vector search spaces, fine-tuning approximate nearest neighbor (ANN) graph parameters, HNSW cluster metrics, IVF index lists, and scalar quantization bounds.
  
• Configure advanced hybrid search architectures, engineering unified retrieval execution flows combining semantic vector lookups with traditional full-text keyword querying (BM25).
  
• Implement strict metadata filtering schemas, constructing optimized filter patterns to speed up context retrieval times and enforce dynamic domain isolation safety parameters.
  
• Monitor cluster metrics and resource optimization loops, tracking vector pod memory allocations, index reconstruction latencies, query-per-second (QPS) thresholds, and compute costs.
  
• Collaborate with AI and Data Engineering squads to evaluate text embedding models (e.g., OpenAI , Cohere , Hugging Face ) and map vector sizing requirements cleanly to downstream application runtimes.
  

Requirements

• 4 to 8 years of core enterprise data engineering, database administration, or backend software development experience, with 2+ dedicated years actively scaling high-dimensional vector database frameworks.
  
• Strong technical mastery of Python, advanced SQL, vector similarity distance metrics (Cosine, Euclidean, Dot Product), and data transformation engines (e.g., Spark, dbt).
  
• Deep structural understanding of index types (HNSW, IVF, Flat), metadata index caching, memory footprint constraints, and cloud tenant auto-scaling mechanics.
  
• Mandatory certification: Official Vector DB specialized credential or a Professional Cloud Data Engineer certificate (AWS/GCP/Azure).
  

Preferred Qualifications

• Prior experience implementing real-time change data capture (CDC) architectures to automatically sync operational databases with vector catalogs.
  
• Familiarity with orchestration tools like LangChain , LangGraph , or LlamaIndex to structure retrieval steps for Retrieval-Augmented Generation (RAG) pipelines.