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Vector Databases Jobs (NOW HIRING)

AI Data Engineer

Cupertino, CA · On-site

$141K - $169K/yr

You will design and implement data pipelines that ingest from legal systems, transform data into AI-ready formats, load vector databases and other AI stores, and expose data services through APIs.

Senior Data Engineer, AI Platform

San Jose, CA · On-site

$124K - $168K/yr

Familiarity with vector databases and ANN search systems * Experience in data systems for AI platforms or ML infrastructure * Background in search, recommendation systems, or information retrieval ...

Develop and implement AI solutions using Python and AI frameworks such as Langgraph and Langchain Work with vector databases like Pinecone to manage and query highdimensional data Build and maintain ...

AIML Engineer

Mason, OH · On-site

$95K - $165K/yr

Proven experience with RAG architectures, embeddings, and vector databases * Experience with agentic frameworks (e.g., LangChain, LangGraph, AutoGen) * Strong system design skills with experience ...

... vector databases • build scalable use cases like semantic search, question answering, and document clustering Qualifications : Required : • Proficiency in Python and ML libraries (TensorFlow ...

The role focuses on Retrieval Augmented Generation (RAG), semantic search, vector databases, metadata engineering, and enterprise knowledge orchestration to deliver secure, accurate, and context ...

New

AI/ML Engineer

Burbank, CA · On-site

$111K - $153K/yr

Implement vector search solutions using vector databases or MongoDB * Ensure CI/CD integration and cloud deployment (Azure preferred) * Establish observability, monitoring, and evaluation frameworks ...

Vector databases & embeddings (Azure) * LLM evaluation, latency optimization, cost management, hallucination mitigation * AI governance, PII handling, security, and enterprise compliance

New

Remote AI Architect

Boston, MA · Remote

$90 - $92/hr

Experience with MLOps/LLMOps ecosystems, including tools such as MLflow, Kubernetes, LangChain, vector databases, and feature stores. * Strong hands on experience with ML frameworks, LLM platforms ...

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Vector Databases information

What is the salary of a vector database developer?

The salary of a vector database developer typically ranges from $80,000 to $150,000 annually, depending on experience, location, and company size. Skilled developers with expertise in machine learning, data structures, and database management may earn higher salaries, especially in tech hubs or with advanced certifications.

Are vector databases the future?

Vector database jobs involve managing and optimizing databases designed for high-dimensional vector data, which are essential for AI and machine learning applications. As AI continues to grow, demand for professionals skilled in vector database technologies and related tools like embedding models is expected to increase, making this a promising field for future job opportunities.

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?

AspectVector DatabasesData Engineers
Required SkillsDatabase management, data modeling, query optimizationData pipeline development, ETL processes, programming
Work EnvironmentData storage systems, AI/ML projects, cloud platformsData infrastructure, cloud environments, big data tools
Industry UsageAI, machine learning, recommendation systemsData 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 can you do with a vector database?

A vector database is used in roles involving data management and machine learning to store, search, and retrieve high-dimensional vector representations of data such as images, text, or audio. It enables efficient similarity searches, supporting applications like recommendation systems, natural language processing, and computer vision. Working with a vector database often requires knowledge of data structures, indexing techniques, and programming skills in languages like Python or C++.

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.

What are the top 5 vector databases?

Top vector databases used in data management and AI applications include Pinecone, Weaviate, FAISS, Milvus, and Annoy. These databases are optimized for storing and searching high-dimensional vector data, often requiring skills in machine learning and database management. They are widely adopted for tasks like similarity search and recommendation systems.
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Infographic showing various Vector Databases job openings in the United States as of July 2026, with employment types broken down into 60% Full Time, and 40% Contract. Highlights an 90% In-person, and 10% Remote job distribution.

$110K - $120K/yr

Full-time

Re-posted 26 days ago


Job description

Gen AI Developer
Key Responsibilities
• Design and develop agentic AI solutions using LangChain, LangGraph, and A2A communication protocols
• Implement RAG (Retrieval-Augmented Generation) pipelines leveraging Azure AI Search and vector databases
• Build and orchestrate multi-agent workflows and intelligent automation systems
• Develop backend services using Java Spring Boot for integration, APIs, and enterprise workflows
• Create responsive front-end applications using React JS
• Integrate OpenAI / LLM models for reasoning, generation, and decision-making capabilities
• Manage and optimize vector storage solutions using PGVector or ChromaDB
• Ensure scalability, performance, and security of AI-driven applications
• Collaborate with cross-functional teams to translate business use cases into AI-powered solutions
Required Skills
• Strong programming skills in Python and Java (Spring Boot)
• Hands-on experience with LangChain, LangGraph, and Agentic AI frameworks
• Deep understanding of RAG architecture and enterprise search (Azure AI Search)
• Experience with OpenAI or similar LLM platforms
• Knowledge of vector databases (PGVector, ChromaDB)
• Front-end development experience with React JS
• Understanding of agent orchestration and distributed AI systems
Nice to Have
• Experience with multi-agent systems and orchestration frameworks
• Exposure to enterprise AI architecture and cloud platforms (Azure preferred)
• Knowledge of AI governance, evaluation, and monitoring frameworks
Salary Range- $110,000-$120,000 a year
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