[Hiring] Vector Database Specialist @FyerX

Role Description

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.

Qualifications

• 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.