Experience with vector databases and/or MongoDB Qualifications: * 8-10 years of experience
Experience with vector databases and/or MongoDB Qualifications: * 8-10 years of experience
Generative AI Architect (GenAI / LLM Architect)
Mclean, VA · On-site
$63.75 - $84/hr
Design and manage vector databases : * Pinecone, FAISS, Weaviate, Milvus, Chroma * Enable semantic search, embeddings, and knowledge retrieval systems Cloud & Deployment * Architect and deploy AI ...
Generative AI Architect (GenAI / LLM Architect)
Mclean, VA · On-site
$63.75 - $84/hr
Design and manage vector databases : * Pinecone, FAISS, Weaviate, Milvus, Chroma * Enable semantic search, embeddings, and knowledge retrieval systems Cloud & Deployment * Architect and deploy AI ...
AI ML Engineer
Woodland Hills, CA · On-site
$140K - $160K/yr
... Embedding generation o Vector database integration • Databases o Vector Databases o MongoDB • Production-grade ML Engineering o Scalable, production-ready ML/GenAI solutions Roles ...
AI ML Engineer
Woodland Hills, CA · On-site
$140K - $160K/yr
... Embedding generation o Vector database integration • Databases o Vector Databases o MongoDB • Production-grade ML Engineering o Scalable, production-ready ML/GenAI solutions Roles ...
Implement semantic search and document retrieval systems using vector databases to support AI-driven knowledge retrieval. * Enhance and maintain the existing client AI Chat Tool, improving user ...
Quick apply
Implement semantic search and document retrieval systems using vector databases to support AI-driven knowledge retrieval. * Enhance and maintain the existing client AI Chat Tool, improving user ...
Implement semantic search and document retrieval systems using vector databases to support AI-driven knowledge retrieval. * Enhance and maintain the existing client AI Chat Tool, improving user ...
Implement semantic search and document retrieval systems using vector databases to support AI-driven knowledge retrieval. * Enhance and maintain the existing client AI Chat Tool, improving user ...
Experience with Redis, Temporal, vector databases, or async job processing frameworks * Experience with change data capture (Debezium or similar), large-scale data migration, or streaming data ...
Experience with Redis, Temporal, vector databases, or async job processing frameworks * Experience with change data capture (Debezium or similar), large-scale data migration, or streaming data ...
Python AI Developer
Irving, TX · On-site
$90K - $115K/yr
Working knowledge of retrieval-augmented generation (RAG) pipelines and vector databases Roles & Responsibilities • Build and tune LLM-based applications using platforms like Vertex, GPT, Lamma ...
Python AI Developer
Irving, TX · On-site
$90K - $115K/yr
Working knowledge of retrieval-augmented generation (RAG) pipelines and vector databases Roles & Responsibilities • Build and tune LLM-based applications using platforms like Vertex, GPT, Lamma ...
Implement semantic search and document retrieval systems using vector databases to support AI-driven knowledge retrieval. * Enhance and maintain the existing client AI Chat Tool, improving user ...
Implement semantic search and document retrieval systems using vector databases to support AI-driven knowledge retrieval. * Enhance and maintain the existing client AI Chat Tool, improving user ...
Senior Data Science Engineer
Woodland Hills, CA · On-site
$140K - $160K/yr
The ideal candidate will have deep expertise in Python, hands-on experience in model training, document processing pipelines, and strong knowledge of vector databases and modern ML/GenAI frameworks.
Senior Data Science Engineer
Woodland Hills, CA · On-site
$140K - $160K/yr
The ideal candidate will have deep expertise in Python, hands-on experience in model training, document processing pipelines, and strong knowledge of vector databases and modern ML/GenAI frameworks.
Work with vector databases and embeddings * Optimize AI model performance Required Skills * Strong experience with Python and NLP libraries * Experience with Transformers, LangChain, OpenAI APIs, or ...
Work with vector databases and embeddings * Optimize AI model performance Required Skills * Strong experience with Python and NLP libraries * Experience with Transformers, LangChain, OpenAI APIs, or ...
The ideal candidate will have hands-on expertise in prompt engineering, fine-tuning LLMs, and working with LangChain and vector databases , while also ensuring robust API and backend development on ...
The ideal candidate will have hands-on expertise in prompt engineering, fine-tuning LLMs, and working with LangChain and vector databases , while also ensuring robust API and backend development on ...
Build RAG-based applications integrating embeddings and vector databases. Implement agentic workflows and reasoning strategies for task automation. Integrate MCP (Model Context Protocols) into ...
Build RAG-based applications integrating embeddings and vector databases. Implement agentic workflows and reasoning strategies for task automation. Integrate MCP (Model Context Protocols) into ...
Production experience deploying and managing vector databases (Milvus, Qdrant, or Weaviate) at scaleExperience with PostgreSQL or MySQL in production environmentsUnderstanding of RAG pipelines ...
Production experience deploying and managing vector databases (Milvus, Qdrant, or Weaviate) at scaleExperience with PostgreSQL or MySQL in production environmentsUnderstanding of RAG pipelines ...
The ideal candidate will possess deep expertise in Generative AI, Large Language Models (LLMs), vector databases, cloud AI deployments, and Responsible AI practices . This role requires a strategic ...
The ideal candidate will possess deep expertise in Generative AI, Large Language Models (LLMs), vector databases, cloud AI deployments, and Responsible AI practices . This role requires a strategic ...
Gen AI Engineer
Phoenix, AZ · On-site
Develop production-grade AI applications utilizing LLMs, RAG architectures, vector databases, and advanced AI orchestration frameworks. * Design and operationalize large-scale data pipelines to ...
New
Gen AI Engineer
Phoenix, AZ · On-site
Develop production-grade AI applications utilizing LLMs, RAG architectures, vector databases, and advanced AI orchestration frameworks. * Design and operationalize large-scale data pipelines to ...
New
The ideal candidate will have hands-on expertise in prompt engineering, fine-tuning LLMs, and working with LangChain and vector databases, while also ensuring robust API and backend development on ...
The ideal candidate will have hands-on expertise in prompt engineering, fine-tuning LLMs, and working with LangChain and vector databases, while also ensuring robust API and backend development on ...
AI AWS Technical Architect
Parsippany, NJ · On-site
$65 - $85.50/hr
Vector databases * Prompt engineering * Experience with vector databases such as: * Qdrant * Pinecone * OpenSearch * MongoDB Atlas Vector Search * Expertise in: * CI/CD pipelines * MLOps * Kubernetes
AI AWS Technical Architect
Parsippany, NJ · On-site
$65 - $85.50/hr
Vector databases * Prompt engineering * Experience with vector databases such as: * Qdrant * Pinecone * OpenSearch * MongoDB Atlas Vector Search * Expertise in: * CI/CD pipelines * MLOps * Kubernetes
... tuning Vector Databases & RAG Practical experience with open-source vector databases such as Qdrant, Chroma, Milvus, or pgvector Proven implementation of Retrieval-Augmented Generation (RAG ...
New
... tuning Vector Databases & RAG Practical experience with open-source vector databases such as Qdrant, Chroma, Milvus, or pgvector Proven implementation of Retrieval-Augmented Generation (RAG ...
New
AIML Engineer
Mason, OH · On-site
... vector databases • Experience with agentic frameworks (e.g., LangChain, LangGraph, AutoGen) • Strong system design skills with experience building and scaling cloud-based applications Roles ...
AIML Engineer
Mason, OH · On-site
... vector databases • Experience with agentic frameworks (e.g., LangChain, LangGraph, AutoGen) • Strong system design skills with experience building and scaling cloud-based applications Roles ...
Design and integrate vector database solutions for efficient semantic search * Perform model quantization and performance tuning for CPU-based inference * Ensure data privacy, security, and ...
Design and integrate vector database solutions for efficient semantic search * Perform model quantization and performance tuning for CPU-based inference * Ensure data privacy, security, and ...
Vector Databases information
What are vector databases?
Vector databases are specialized databases designed to store, manage, and search high-dimensional vector data, which is commonly generated from machine learning models, such as embeddings from natural language processing or image recognition. They enable efficient similarity search operations, such as finding the most similar items to a given query vector, which is essential for applications like recommendation systems, semantic search, and AI-powered search engines. Unlike traditional databases that handle structured or unstructured data, vector databases are optimized for fast and scalable similarity searches on large datasets of vectors.
What are some common challenges faced when working with vector databases, and how can they be addressed?
Professionals working with vector databases often encounter challenges such as efficiently scaling to handle large datasets, ensuring low-latency similarity searches, and integrating the database with machine learning pipelines. To address these, teams typically implement distributed architectures, fine-tune indexing strategies, and collaborate closely with data engineers and machine learning specialists. Staying updated with the latest developments in vector database technologies and maintaining clear communication with cross-functional teams are also key to overcoming these challenges.
What is the difference between Vector Databases vs Data Engineers?
| Aspect | Vector Databases | Data Engineers |
|---|---|---|
| Required Skills | Database management, data modeling, query optimization | Data pipeline development, ETL processes, programming |
| Work Environment | Data storage systems, AI/ML projects, cloud platforms | Data infrastructure, cloud environments, big data tools |
| Industry Usage | AI, machine learning, recommendation systems | Data integration, analytics, data architecture |
While Vector Databases focus on storing and querying high-dimensional vector data for AI applications, Data Engineers build and maintain data pipelines and infrastructure to support data analysis and machine learning workflows. Both roles are essential in data-driven industries but serve different functions within the data ecosystem.
What are the key skills and qualifications needed to thrive as a Vector Database Engineer, and why are they important?
Success as a Vector Database Engineer requires a strong background in computer science, database management, and experience with machine learning or AI-driven data systems. Familiarity with vector database platforms (such as Pinecone, Milvus, or Weaviate), cloud infrastructure, and proficiency in languages like Python are typically expected. Strong problem-solving skills, effective communication, and the ability to work cross-functionally help engineers stand out. These competencies are vital to efficiently design, deploy, and maintain scalable vector search solutions that power modern AI applications.
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Full-time
Posted 22 days ago
Job description
Role Overview:
The Data Science Engineer will develop scalable ML and Generative AI solutions, specializing in model training, document processing pipelines, and vector search implementations. Strong Python expertise and experience across modern ML and GenAI workflows are essential.
Key Responsibilities:
Required Skills:
Qualifications:
The Data Science Engineer will develop scalable ML and Generative AI solutions, specializing in model training, document processing pipelines, and vector search implementations. Strong Python expertise and experience across modern ML and GenAI workflows are essential.
Key Responsibilities:
- Develop and deploy Machine Learning and Generative AI solutions using Python
- Design and refine prompt engineering strategies for LLM applications
- Build document extraction, parsing, and chunking pipelines
- Train, evaluate, and fine-tune ML models; manage tagging and labeling workflows
- Implement embedding generation and vector search solutions
- Integrate ML models with vector databases and MongoDB
- Ensure code quality, scalability, and production readiness
Required Skills:
- Expert-level proficiency in Python
- Strong experience with model training, evaluation, and tagging workflows
- Hands-on experience with document extraction and chunking techniques
- Solid understanding of ML algorithms and Generative AI concepts
- Experience with vector databases and/or MongoDB
Qualifications:
- 8-10 years of experience
About Prophecy Consulting
Sourced by ZipRecruiter
Company size
11 - 50 Employees
Headquarters location
Irvine, CA, US
Year founded
2018