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Vector Databases Jobs in Washington, DC (NOW HIRING)

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 ...

Enterprise Architect

Mclean, VA ยท On-site

$70.75 - $91.25/hr

Specialize in large language models and frameworks, vector databases, and cloud deployments. * Implement Responsible AI techniques, including strategy and execution. Skills * Expertise in AI ...

AI Engineer

Reston, VA ยท On-site

$75K - $190K/yr

Extensive experience working with vector technology databases, designing and implementing solutions to efficiently store, search, and analyze high-dimensional data for real-time and large-scale ...

Extensive experience working with vector technology databases, designing and implementing solutions to efficiently store, search, and analyze high-dimensional data for real-time and large-scale ...

AI Engineer

Reston, VA ยท On-site

$75K - $190K/yr

Extensive experience working with vector technology databases, designing and implementing solutions to efficiently store, search, and analyze high-dimensional data for real-time and large-scale ...

Manage and optimize vector databases (e.g., Pinecone, Weaviate, Milvus) * Design and optimize Retrieval-Augmented Generation (RAG) pipelines for performance and scalability * Implement AI governance ...

Senior GenAI Engineer

Reston, VA ยท On-site

$108K - $149K/yr

The ideal candidate will have strong expertise in Python-based backend development, LLM -powered applications, cloud-native deployment, vector databases, and modern DevOps practices . This role ...

Architect and operationalize RAG pipelines , embeddings, vector databases, and LLM-powered solutions (chatbots, summarization, semantic search, anomaly detection). * Implement CI/CD pipelines (GitHub ...

Manage and optimize vector databases (e.g., Pinecone, Weaviate, Milvus) * Design and optimize Retrieval-Augmented Generation (RAG) pipelines for performance and scalability * Implement AI governance ...

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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.

Platform Architect

Purple Drive

Mclean, VA โ€ข On-site

Other

Posted 11 days ago


Job description

Overview:
Role: Platform Architect
Skills: AI & Gen AI - Products & Tools
AI Architect
  • Create overarching solution architecture.
  • Define the vision for AI programs with extensive experience.
  • Establish AI standards and guidelines for the enterprise.
  • Specialize in large language models and frameworks, vector databases, and cloud deployments.
  • Implement Responsible AI techniques, including strategy and execution.

Skills
  • Master's degree in computer science with 8 years of experience
  • Expertise in AI specialization, particularly in large language models and frameworks
  • Experience with vector databases and cloud deployments.