ZipRecruiter

Log In

1

Vector Databases Jobs in Maryland (NOW HIRING)

SageCor Solutions

AI Engineer (PPA - 009)

Columbia, MD · On-site

... vector databases, embeddings, model APIs, evaluation frameworks, and agent testing harnesses. • Exposure to model hosting, inference pipelines, or cloud-based AI development environments. • ...

Markon

Senior Data Scientist

Annapolis Junction, MD

$150K - $210K/yr

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

Markon

Senior Data Scientist

Annapolis Junction, MD · On-site

$150K - $210K/yr

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

New

Markon

Senior Data Scientist

Annapolis Junction, MD · On-site

$150K - $210K/yr

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

New

Leidos

Software Engineer

Cabin John, MD · On-site

$131K - $237K/yr

... tools, vector databases, and microservices-based architectures. • Write high-quality, maintainable code in multiple programming languages (e.g., Python, Java, C++, Go), leveraging AI-assisted ...

next page

Showing results 1-20

All JobsVector Databases JobsVector Databases Jobs in Maryland

Vector Databases information

What is the salary of a vector database developer?

The salary of a vector database developer typically ranges from $80,000 to $150,000 annually, depending on experience, location, and company size. Skilled developers with expertise in machine learning, data structures, and database management may earn higher salaries, especially in tech hubs or with advanced certifications.

Are vector databases the future?

Vector database jobs involve managing and optimizing databases designed for high-dimensional vector data, which are essential for AI and machine learning applications. As AI continues to grow, demand for professionals skilled in vector database technologies and related tools like embedding models is expected to increase, making this a promising field for future job opportunities.

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 can you do with a vector database?

A vector database is used in roles involving data management and machine learning to store, search, and retrieve high-dimensional vector representations of data such as images, text, or audio. It enables efficient similarity searches, supporting applications like recommendation systems, natural language processing, and computer vision. Working with a vector database often requires knowledge of data structures, indexing techniques, and programming skills in languages like Python or C++.

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 are the top 5 vector databases?

Top vector databases used in data management and AI applications include Pinecone, Weaviate, FAISS, Milvus, and Annoy. These databases are optimized for storing and searching high-dimensional vector data, often requiring skills in machine learning and database management. They are widely adopted for tasks like similarity search and recommendation systems.
What are popular job titles related to Vector Databases jobs in Maryland? For Vector Databases jobs in Maryland, the most frequently searched job titles are:
What job categories do people searching Vector Databases jobs in Maryland look for? The top searched job categories for Vector Databases jobs in Maryland are:
What cities in Maryland are hiring for Vector Databases jobs? Cities in Maryland with the most Vector Databases job openings:
Vector Databases jobs near you

AI Engineer (PPA - 009)

SageCor Solutions

Columbia, MD • On-site

Full-time

Posted 21 days ago

Job description

Description
Serving Maryland and the Greater Washington D.C. area, SageCor Solutions (SageCor) is a growing company bringing complete engineering services and true full lifecycle System Engineering services to areas requiring (or desiring) nationally-recognized expertise in high performance computing, large data analytics and cutting edge information technologies.
Active TS/SCI w/ Polygraph required.
Required Experience:
  • Min 18 years with Bachelors, 15 years with Masters degree in Computer Science, Engineering, Data Science, Mathematics, or related field.

Required Skills:
  • Experience with Python, JavaScript/TypeScript, or similar languages commonly used in AI application development.
  • Familiarity with vector databases, embeddings, model APIs, evaluation frameworks, and agent testing harnesses.
  • Exposure to model hosting, inference pipelines, or cloud-based AI development environments.
  • Understanding of secure AI development practices, data protection, and operational constraints in government settings.
  • Experience supporting defense, cyber, intelligence, or mission application development.
  • Familiarity with human-in-the-loop workflows, AI assurance, and evaluation methodologies.

Consistent with federal and state law where SageCor conducts business, SageCor Solutions provides equal employment opportunities (EEO) to all employees and applicants for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability or veteran status, or any other protected class.

Apply