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Vector Databases Jobs in Washington, DC (NOW HIRING)

Alpha Consulting Corp.

Gen AI Architect

Mclean, VA · On-site

$63.75 - $84/hr

... vector databases, and cloud deployments. • Implement Responsible AI techniques, including strategy and execution. Qualifications : Required : • AI Architect- Create overarching solution ...

Scalence L.L.C.

Gen AI Architect

Mclean, VA · On-site

$63.75 - $84/hr

... vector databases, and cloud deployments. • Implement Responsible AI techniques, including strategy and execution. Qualifications : Required : • AI Architect- Create overarching solution ...

Virtual Networx

AI/ML Engineer

Mclean, VA · On-site

Python, Machine Learning, Deep Learning, Scikit-learn, TensorFlow, PyTorch, Pandas, NumPy, SQL, NLP, Computer Vision, Generative AI, LLM, Prompt Engineering, RAG, Vector Databases, REST APIs ...

Amivero

AI Engineer

Reston, VA

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 ...

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 ...

Amivero

AI Engineer

Reston, VA · On-site

... vector databases such as OpenSearch, Pinecone, Weaviate, Chroma, FAISS, or similar technologies for scalable retrieval systems. • Develop data ingestion and knowledge management pipelines to ...

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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 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

Lead Software Engineer (Python, Vector Databases, AWS)

Socket.dev

Mclean, VA • On-site

$197 - $225/hr

Other

Posted 8 days ago

Job description

Lead Software Engineer (Python, Vector Databases, AWS)

Do you love building and pioneering in the technology space? Do you enjoy solving complex business problems in a fast-paced, collaborative, inclusive, and iterative delivery environment?

At Capital One, you’ll be part of a big group of makers, breakers, doers and disruptors, who solve real problems and meet real customer needs. We are seeking Full Stack Software Engineerswho are passionate about marrying data with emerging technologies. As a Capital One Senior Lead Software Engineer, you’ll have the opportunity to be on the forefront of driving a major transformation within Capital One.

As a Lead Software Engineer, you will build the data infrastructure layer that powers Capital One’s AI-driven content management capabilities. You will own the Content Hub — our vector and graph database platform for semantic content retrieval — including the embedding pipelines, knowledge graph schema, and indexing infrastructure that enable AI models to retrieve the right content, rules, and context at generation time.

What You’ll Do:
  • Design and build the Content Hub - vector database and graph database infrastructure for semantic retrieval. Own the indexing pipeline, query layer, and integration with AI content generation platform.
  • Build and operate embedding pipelines, generate embeddings, manage chunking strategies, and handle index refresh as content evolves.
  • Own embedding model selection and refresh strategy — evaluate tradeoffs between embedding model quality and operational cost; design refresh pipelines that keep the index current as the underlying content corpora changes.
  • Lead a portfolio of diverse technology projects and a team of developers with deep experience in distributed microservices, and full stack systems to create solutions that help meet regulatory needs for the company
  • Share your passion for staying on top of tech trends, experimenting with and learning new technologies, participating in internal & external technology communities, mentoring other members of the engineering community, and from time to time, be asked to code or evaluate code
  • Collaborate with digital product managers, and deliver robust cloud-based solutions that drive powerful experiences to help millions of Americans achieve financial empowerment
  • Utilize programming languages like JavaScript, Java, HTML/CSS, TypeScript, SQL, Python, and Go, Open Source RDBMS and NoSQL databases, Container Orchestration services including Docker and Kubernetes, and a variety of AWS tools and services
Basic Qualifications:
  • Bachelor’s Degree
  • At least 4 years of experience in software engineering (Internship experience does not apply)
  • At least 1 year experience with cloud computing (AWS, Microsoft Azure, Google Cloud)
Preferred Qualifications:
  • Experience with data infrastructure, search systems, or AI/ML engineering
  • Experience working with vector databases or embedding-based search
  • Experience working with data pipeline engineering — ingestion, transformation, chunking, and indexing at scale; familiarity with batch and streaming pipeline patterns
  • Experience with Python for data pipeline and backend service development
  • Experience with RAG architecture
  • 2+ years of experience in Agile practices
  • Experience leveraging interactive AI tooling to accelerate productivity, utilizing capabilities beyond basic code completion

At this time, Capital One will not sponsor a new applicant for employment authorization, or offer any immigration related support for this position (e.g. H1B, F-1 OPT, F-1 STEM OPT, F-1 CPT, J-1, TN, E-3, and O-1, or any other forms of work authorization that require immigration support from an employer).

The minimum and maximum full-time annual salaries for this role are listed below, by location. Please note that this salary information is solely for candidates hired to perform work within one of these locations, and refers to the amount Capital One is willing to pay at the time of this posting. Salaries for part-time roles will be prorated based upon the agreed upon number of hours to be regularly worked.

McLean, VA: $197,300 - $225,100 for Lead Software Engineer

Richmond, VA: $179,400 - $204,700 for Lead Software Engineer

Candidates hired to work in other locations will be subject to the pay range associated with that location, and the actual annualized salary amount offered to any candidate at the time of hire will be reflected solely in the candidate’s offer letter.

This role is also eligible to earn performance based incentive compensation, which may include cash bonus(es) and/or long term incentives (LTI). Incentives could be discretionary or non discretionary depending on the plan.

Capital One offers a comprehensive, competitive, and inclusive set of health, financial and other benefits that support your total well-being. Eligibility varies based on full or part-time status, exempt or non-exempt status, and management level.

Capital One is an equal opportunity employer (EOE, including disability/vet) committed to non-discrimination in compliance with applicable federal, state, and local laws. Capital One promotes a drug-free workplace.

Capital One will consider for employment qualified applicants with a criminal history in a manner consistent with the requirements of applicable laws regarding criminal background inquiries, including, to the extent applicable, Article 23-A of the New York Correction Law; San Francisco, California Police Code Article 49, Sections 4901-4920; New York City’s Fair Chance Act; Philadelphia’s Fair Criminal Records Screening Act; and other applicable federal, state, and local laws and regulations regarding criminal background inquiries.

If you have visited our website in search of information on employment opportunities or to apply for a position, and you require an accommodation, please contact Capital One Recruiting at 1-800-304-9102 or via email at RecruitingAccommodation@capitalone.com. All information you provide will be kept confidential and will be used only to the extent required to provide needed reasonable accommodations.

For technical support or questions about Capital One’s recruiting process, please send an email to Careers@capitalone.com

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