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Vector Databases Jobs (NOW HIRING)

Varite

USA_Engineer

Falls Church, VA · On-site

$53.57 - $55.14/hr

... frameworks| vector databases| and cloud deployments. - Implement Responsible AI techniques| including strategy and execution - Master's degree in computer science with 8 years of experience ...

Prophecy Technologies

Senior Java Developer

Sunrise, FL · On-site

$54.50 - $69.50/hr

Integrate LLMs with mcp servers, vector databases, and observability systems for adaptive agent behavior * Ensure reliability, performance, and maintainability through rigorous testing, type safety ...

Mindlance

AI Software Engineer

Hopkins, MN

... 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 ...

Prophecy Technologies

Azure Open AI Engineer

Malvern, PA · On-site

$54 - $67/hr

The ideal candidate will have strong expertise in Azure Open AI, including pipeline development, vector database configuration, and secure API integration. This role offers an exciting opportunity to ...

Purple Drive Technologies

Gen AI architect

Mclean, VA · On-site

$63.75 - $84/hr

Architect and deploy solutions leveraging vector databases for embedding storage and semantic search. * Design scalable AI solutions deployed in cloud environments (Azure, AWS, or GCP). * Implement ...

eTeam

AI Software Engineer

Hopkins, MN · Hybrid

... 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 ...

Entropydb

Senior Database Engineer

Richmond, VA · On-site +1

$104K - $142K/yr

... Knowledge of vector databases and embeddings Experience with Kubernetes and cloud platforms We invest in our team with comprehensive benefits designed to support your growth and well-being.

Photon

AI Developer (Platform + Backend)

Tempe, AZ

Integrate Generative AI services, LLMs, vector databases, and semantic search capabilities. Build and implement agent-based workflows including multi-step execution, tool integrations, and AI-driven ...

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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?

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 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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Vector Databases jobs near you

AIML Engineer

Purple Drive Technologies LLC

Mason, OH

Other

Posted 5 days ago

Job description

Must Have Technical/Functional Skill

Strong Python expertise with experience building and deploying production-grade backend systems

Hands-on experience developing applications using LLMs, including prompt engineering and orchestration

Proven experience with RAG architectures, embeddings, and vector databases

Experience with agentic frameworks (e.g., LangChain, LangGraph, AutoGen)

Strong system design skills with experience building and scaling cloud-based applications

Roles & Responsibilities

Architect and deliver end-to-end LLM-powered applications and agentic workflows using Python

Design and implement RAG pipelines over enterprise data using embeddings and vector databases

Build multi-step, tool-using agents (planning, execution, memory) using frameworks such as

LangChain

Integrate AI systems with APIs, backend services, and cloud platforms

Establish evaluation, reliability, and performance strategies (accuracy, latency, cost)

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