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Vector Databases Jobs in Maryland (NOW HIRING)

The Josef Group

Senior Data Scientist

Columbia, MD · On-site

Hands-on experience with SQL, NoSQL, graph (Neo4j), and vector databases * Experience working in AWS and/or Azure cloud environments Preferred Qualifications: * Experience with Hadoop, Spark, Kafka ...

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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SixGen, Inc.

Data Scientist (TS/SCI w/ Poly)

Columbia, MD · On-site

$180K - $220K/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 ...

SixGen, Inc.

Data Scientist (TS/SCI w/ Poly)

Columbia, MD · On-site

$180K - $220K/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 ...

Endictus

Senior Data Scientist

Columbia, MD · On-site

$146K/yr

Develop and manage data architectures utilizing graph, relational, NoSQL, and vector databases. * Optimize data storage, retrieval, and access strategies to improve AI model performance and ...

Endictus

Senior Data Scientist

Columbia, MD

$146K/yr

Develop and manage data architectures utilizing graph, relational, NoSQL, and vector databases. * Optimize data storage, retrieval, and access strategies to improve AI model performance and ...

Endictus Corp

Senior Data Scientist

Columbia, MD · On-site

$146K/yr

Develop and manage data architectures utilizing graph, relational, NoSQL, and vector databases. * Optimize data storage, retrieval, and access strategies to improve AI model performance and ...

Bigbear.ai

Full Stack Software Engineer

Columbia, MD · On-site

$114K/yr

Vector Databases: Knowledge of vector databases and embedding systems * Distributed Systems: Experience with high-performance computing or distributed systems Pay transparency Please note the ...

BigBear.ai

Full Stack Software Engineer

Columbia, MD · On-site

Vector Databases: Knowledge of vector databases and embedding systems * Distributed Systems: Experience with high-performance computing or distributed systems Pay transparency Please note the ...

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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.
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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

Senior Data Scientist (Senior Software Developer; Systems Softwa with Security Clearance

Avid Technology Professionals

Columbia, MD • On-site

Other

Re-posted yesterday

Job description

Advanced proficiency in programming languages commonly used for data engineering (e.g., Python, Java, Scala) Demonstrated expertise in designing, developing, and optimizing data pipelines for large-scale enterprise environments Proven experience with corporate dataflows and developing data parsers for extremely large datasets Extensive experience with various database technologies including graph databases (e.g., Neo4j), SQL databases (e.g., PostgreSQL, MySQL), NoSQL databases (e.g., MongoDB, Cassandra), and vector databases Familiarity with cloud platforms (AWS, Microsoft Azure) for data storage and processing Qualifications - Degree from an accredited college or university in System Engineering or related discipline and one of the following: - 10+ years of experience with a Master's degree - 12+ years of experience with a Bachelor's degree - 16+ years of experience with Associate's Degree or HS Diploma Desired: Experience with data governance, data security, and compliance best practices Familiarity with big data technologies such as Hadoop, Spark, or Kafka. Experience with data warehousing concepts and tools Continuous learning mindset to stay abreast of cutting-edge data engineering and AI advancements Understanding of machine learning concepts and their implications for data infrastructure Excellent communication and interpersonal skills, with the ability to effectively collaborate with cross-functional teams Ability to translate complex data requirements into actionable engineering solutions

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