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Vector Databases Jobs in Ashburn, VA (NOW HIRING)

Gurus Infotech, Inc.

AI QE Architect

Bethesda, MD · On-site

Hands-on experience with LLMs, prompt engineering, RAG, vector databases, and model evaluation * Strong automation expertise using Playwright, Selenium, PyTest, RestAssured, and JMeter/Locust

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Tech Tammina

AI/ML Engineer

Reston, VA · On-site

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

Interon IT Solutions

AI/ML Engineer

Reston, VA · On-site

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

Noblis

AI Solutions Lead IV

Chantilly, VA · On-site

Leads platform and model integration across cloud services (Azure, AWS, or GCP), APIs, vector databases, orchestration frameworks, embeddings, vector search, memory, and reasoning workflows.

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Showing results 41-60

All JobsVector Databases JobsVector Databases Jobs in Ashburn, VA

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
Infographic showing various Vector Databases job openings in Ashburn, VA as of August 2026, with employment types broken down into 87% Full Time, 5% Part Time, 1% Temporary, and 7% Contract. Highlights an 84% Physical, 5% Hybrid, and 11% Remote job distribution.

Generative AI Engineer (Clearance Required)

InterImage

Arlington, VA • On-site

$60.75 - $83/hr

Full-time

Posted 28 days ago

Job description

InterImage is looking for engineers who thrive where innovation meets execution. Our Product Division rapidly transforms emerging technologies into operational capabilities supporting mission-critical customers. This isn't a research-only position. You'll take Generative AI concepts from proof of concept to production, designing, developing, deploying, and continuously improving AI-powered applications that solve real-world operational problems. If you enjoy building with the latest LLMs, experimenting with emerging AI technologies, and deploying secure cloud-native solutions in Azure, we'd like to meet you.

What You'll Do

  • Design and build Generative AI applications using commercial and open-source Large Language Models (LLMs).
  • Develop proof-of-concepts that evolve into production-ready software.
  • Architect Retrieval-Augmented Generation (RAG) solutions using vector databases and enterprise data sources.
  • Build intelligent agents and AI workflows capable of reasoning, automation, and decision support.
  • Develop REST APIs and backend services supporting AI applications.
  • Deploy scalable AI solutions within Microsoft Azure environments.
  • Design cloud-native architectures using Azure AI Services, Azure OpenAI, Azure Kubernetes Service (AKS), Azure Functions, and Azure Storage.
  • Build CI/CD pipelines supporting rapid AI deployment and model iteration.
  • Integrate AI capabilities into existing enterprise applications and mission systems.
  • Evaluate emerging AI technologies and rapidly prototype new capabilities.
  • Collaborate with software engineers, cloud architects, data scientists, and mission stakeholders to deliver innovative solutions.
  • Optimize model performance, latency, scalability, security, and cost.
  • Implement responsible AI practices, prompt engineering strategies, guardrails, and model evaluation techniques.

Requirements

  • Active Top Secret Clearance
  • 5+ years of software development experience.
  • 2+ years developing Generative AI or Machine Learning applications.
  • Strong Python development experience.
  • Experience working with Large Language Models including GPT, Llama, Claude, Mistral, or similar models.
  • Experience with prompt engineering and AI workflow development.
  • Experience building APIs using FastAPI, Flask, or similar frameworks.
  • Experience deploying cloud-native applications in Microsoft Azure.
  • Familiarity with containerization technologies including Docker and Kubernetes.
  • Experience with Git, CI/CD pipelines, and DevSecOps practices.
  • Strong understanding of software architecture and distributed systems.
Preferred Qualifications
  • Experience with Azure OpenAI Service.
  • Experience building Retrieval-Augmented Generation (RAG) systems.
  • Knowledge of LangChain, LangGraph, Semantic Kernel, LlamaIndex, or similar orchestration frameworks.
  • Experience with vector databases such as Pinecone, Milvus, pgvector, Azure AI Search, or Chroma.
  • Experience developing AI agents and autonomous workflows.
  • Familiarity with MCP (Model Context Protocol) and agent interoperability concepts.
  • Experience with model evaluation, observability, and prompt optimization.
  • Experience deploying AI solutions in secure or classified environments.
  • Familiarity with Infrastructure as Code using Terraform or Bicep.
  • Knowledge of Azure Machine Learning, Azure AI Foundry, or Azure Cognitive Services.