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Vector Databases Jobs in Washington, DC (NOW HIRING)

Amivero

AI Engineer

Reston, VA · 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 ...

Interon IT Solutions

AI/ML Engineer

Reston, VA · On-site

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

Merican

AWS AI Engineer

Herndon, VA · On-site

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 ...

SIXGEN

Data Scientist (TS/SCI w/ Poly)

Columbia, MD · On-site

... vector databases • Collaborate with AI/ML engineers and data scientists to understand data requirements and optimize data access and retrieval for AI models • Ensure data quality, integrity, and ...

Gormat

Senior Data Scientist

Annapolis Junction, MD · On-site

$168K - $183K/yr

Implement and manage various database systems, including graph, SQL, NoSQL, and vector databases * Collaborate with AI/ML engineers and data scientists to understand data requirements and optimize ...

Gormat

Senior Data Scientist

Annapolis Junction, MD · On-site

Implement and manage various database systems, including graph, SQL, NoSQL, and vector databases * Collaborate with AI/ML engineers and data scientists to understand data requirements and optimize ...

SIXGEN

Data Scientist (TS/SCI w/ Poly)

Columbia, MD · On-site

... vector databases • Collaborate with AI/ML engineers and data scientists to understand data requirements and optimize data access and retrieval for AI models • Ensure data quality, integrity, and ...

Noblis

Data Architect IV

Chantilly, VA · On-site

$65.25 - $84/hr

Ensures the scalable, high-performance delivery of graph assets across the enterprise by integrating semantic technologies with cloud platforms, vector databases, and orchestration frameworks to ...

Kapitus

MLOps Architect

Arlington, VA · On-site

$117K - $189K/yr

Vector database integration * Document ingestion and chunking strategies * Retrieval evaluation and monitoring * Design and deploy LLM-based services using: * * Managed services (e.g., SageMaker ...

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Showing results 21-40

All JobsVector Databases JobsVector Databases Jobs in Washington, DC

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 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 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 popular job titles related to Vector Databases jobs in Washington, DC?

For Vector Databases jobs in Washington, DC, the most frequently searched job titles are:

What job categories do people searching Vector Databases jobs in Washington, DC look for?

The top searched job categories for Vector Databases jobs in Washington, DC are:

Vector Databases jobs near you

AI Engineer

Amivero

Reston, VA • On-site

$75K - $190K/yr

Full-time

Re-posted 8 days ago

Job description

Description
Amivero Team
Amivero's team of IT professionals delivers digital services that elevate the federal government, whether national security or improved government services. Our human-centered, data-driven approach is focused on truly understanding the environment and the challenge and reimagining with our customer how outcomes can be achieved.
Our team of technologists leverage modern, agile methods to design and develop equitable, accessible, and innovative data and software services that impact hundreds of millions of people.
As a member of the Amivero team you will use your empathy for a customer's situation, your passion for service, your energy for solutioning, and your bias towards action to bring modernization to very important, mission-critical, and public service government IT systems.
Special Requirements
  • US Citizenship Required to obtain Public Trust
  • Active DHS Clearance (preferred)
  • Bachelor's degree + 6 years of experience
  • 3+ years of experience developing and optimizing solutions using Python or similar, with a strong focus on performance, scalability, and efficiency
  • 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 applications
  • GenAI and Bedrock experience

The Gist...
We are seeking a highly skilled Generative AI Engineer to design, develop, and deploy advanced AI-powered solutions leveraging Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and modern cloud-native architectures. This role will focus on integrating LLMs into enterprise systems, building scalable GenAI applications, optimizing data retrieval pipelines, and developing intelligent solutions using vector databases and AWS-native services such as OpenSearch and Bedrock.
The ideal candidate brings strong hands-on engineering expertise in Python, experience architecting and implementing RAG systems, deep understanding of data chunking and embeddings strategies, and practical knowledge deploying production-grade GenAI solutions.
What Your Day Might Include...
  • Design, build, and deploy LLM-powered applications and intelligent automation solutions for enterprise and mission-focused environments.
  • Integrate Large Language Models (LLMs) into existing systems, workflows, products, and enterprise platforms using APIs, orchestration frameworks, and custom pipelines.
  • Develop scalable Retrieval-Augmented Generation (RAG) architectures that improve response quality, accuracy, explainability, and contextual relevance.
  • Engineer and optimize prompt orchestration, agentic workflows, and inference pipelines for production use.
  • Develop prototypes and production-grade solutions leveraging open-source and commercial foundation models.
  • Architect and implement robust RAG pipelines, including ingestion, indexing, retrieval, reranking, and response generation.
  • Design and optimize data chunking strategies (semantic, recursive, token-based, metadata-aware chunking) to improve retrieval performance and model grounding.
  • Create and manage embedding pipelines for structured and unstructured data sources.
  • Implement and optimize vector search solutions using vector databases and similarity search technologies.
  • Work with vector databases such as OpenSearch, Pinecone, Weaviate, Chroma, FAISS, or similar technologies for scalable retrieval systems.
  • Develop data ingestion and knowledge management pipelines to support enterprise search and GenAI applications.
  • Build and deploy GenAI solutions in cloud-native environments, with preference for AWS Bedrock, Amazon OpenSearch, and related AWS AI/ML services.
  • Integrate LLM applications with enterprise APIs, microservices, databases, and existing application ecosystems.
  • Support deployment of scalable and secure AI services using containers, serverless, and modern DevOps/MLOps practices.
  • Optimize performance, latency, scalability, and observability of GenAI systems in production.
  • Evaluate model performance, retrieval quality, hallucination reduction techniques, and system effectiveness.
  • Implement guardrails, grounding strategies, and responsible AI controls for secure and trustworthy solutions.
  • Stay current on emerging GenAI technologies, frameworks, and architectures, recommending innovations and improvements.
  • Contribute to architecture decisions, technical roadmaps, and GenAI best practices across programs and teams.

Qualifications
  • Bachelor's degree in Computer Science, Engineering, Data Science, or related technical field
  • 5+ years of software engineering or machine learning engineering experience.
  • 2+ years of hands-on experience developing Generative AI / LLM-based solutions.
  • Strong proficiency in Python and experience building production-grade applications.
  • Demonstrated experience integrating LLMs into enterprise systems or applications.
  • Hands-on experience designing and implementing RAG architectures.
  • Strong experience with data chunking strategies, embeddings, and retrieval optimization.
  • Experience with vector databases and semantic search implementations.
  • Experience with GenAI frameworks and tooling such as LangChain, LlamaIndex, Haystack, or similar.
  • Experience with APIs, microservices, and scalable software architectures.

Preferred Qualifications
  • Experience with AWS Bedrock, Amazon OpenSearch, and broader AWS AI/ML ecosystem.
  • Experience working with foundation models such as Claude, Llama, Mistral, OpenAI, or similar.
  • Familiarity with fine-tuning, model evaluation frameworks, and prompt engineering techniques.
  • Experience with MLOps/LLMOps, CI/CD pipelines, Docker, Kubernetes, and cloud deployment patterns.
  • Knowledge of security, governance, and responsible AI considerations for enterprise GenAI implementations.
  • Experience supporting federal, regulated, or enterprise-scale environments is a plus.

EOE/M/F/VET/DISABLED
All qualified applicants will receive consideration without regard to race, color, religion, gender, sexual orientation, gender identity or expression, national origin, age, disability, genetic information, marital status, amnesty, or status as a covered veteran in accordance with applicable federal, state and local laws. Amivero complies with applicable state and local laws governing non-discrimination in employment in every location in which the company has facilities.

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