1

Vector Databases Jobs in Maryland (NOW HIRING)

... and vector search capabilities. • Work with a multi-terabyte literature database and external APIs (PubMed, CrossRef, OpenAlex) to build scalable data processing and analysis pipelines. • ...

Senior AI Engineer

Annapolis Junction, MD · On-site

$114K - $228K/yr

Experience with static analyzers, RAG framework, and relational and vector databases. Fa * Fimiliarization with Agile, Git, Jira, and Confluence. * Collaboratively work with systems engineers to ...

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

Software Engineer 3

Linthicum Heights, MD · On-site

$56.25 - $75.75/hr

... vector databases. • Familiarization with Agile, Git, Jira, and Confluence. Preferred : • Familiarization with RUST, Claude Code, Codex, and CI/CD. Company : Wyetech offers quality engineering ...

Software Engineer 2

Linthicum Heights, MD · On-site

$95K - $130K/yr

... vector databases. • Familiarization with Agile, Git, Jira, and Confluence. Preferred : • Familiarization with RUST, Claude Code, Codex, and CI/CD. Company : Wyetech offers quality engineering ...

Senior AI Engineer (SWE-3)

Linthicum Heights, MD · On-site

$102K - $140K/yr

... vector databases • Familiarization with Agile • Familiarization with Git • Familiarization with Jira • Familiarization with Confluence • Active TS/SCI with Polygraph required Preferred ...

Showing results 41-60

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.
What cities in Maryland are hiring for Vector Databases jobs? Cities in Maryland with the most Vector Databases job openings:

Agentic AI/ DevSecOps Engineer

AP Ventures

Columbia, MD • On-site

Other

Re-posted 12 hours ago


Job description

Title: Agentic AI and DevSecOps Engineer

Location: Hybrid in Columbia, MD - 2x a week
Clearance -   Must be able to obtain/maintain a High-Public Trust Clearance

No C2C will be considered

Company Overview

At APV, we’re more than a technology company — we’re a mission-driven powerhouse transforming organizations through advanced technology and human ingenuity. Our expertise spans AI/ML, data architecture, low-code/no-code development, Agile DevSecOps, and cloud services, delivering scalable and meaningful solutions.

In our Emerging Technology Lab, innovation drives progress. Our teams create intelligent chatbots, AI-powered assistants, robotic process automation (RPA), essay graders, and data analytics platforms. If you’re passionate about solving complex challenges and shaping the future, APV is the place for you. Since 2007, we’ve partnered with federal and state agencies to deliver IT, training, and consulting solutions that achieve mission-critical outcomes. Built on accountability, integrity, and quality, we go beyond expectations. With 70+ prime contracts and a proven record of client success, APV continues to grow — and we’re looking for exceptional talent to grow with us.

At APV, we Always Provide Value

Role:

We are seeking a highly skilled and hands-on Agentic AI/DevSecOps Engineer to join our dynamic team. The ideal candidate will be collaborative, innovative, and capable of providing effective AI solutions for various internal and federal projects. This role requires a deep understanding of legacy applications, emerging technologies, AI technologies, strong problem-solving skills, and the ability to work closely with cross-functional teams.

Duties: 

Agentic System Design & Development

·           Design and build Agentic AI systems using frameworks such as LangChain, Lang Graph, MCP, or similar applications.

·           Develop multi-agent or task-oriented architectures for autonomous workflows

·           Integrate AI agents with enterprise APIs, data platforms, and external tools

·           Define agent workflows, orchestration logic, and tool-use strategies

·           Evaluate when to use LLM-based vs traditional approaches

AI Engineering & Data Systems

·       Build RAG pipelines using embeddings and vector databases

·       Implement vector search and retrieval strategies for knowledge-based agents

·       Work with structured and unstructured data for AI-driven applications

·       Apply AI/ML fundamentals to improve agent reasoning and performance

 Testing, Evaluation & Optimization

·        Develop testing and evaluation pipelines for agentic workflows and LLM systems

·        Implement unit and integration testing for AI-enabled systems

·        Optimize systems for accuracy, latency, cost, and reliability

·        Validate and monitor AI outputs for quality and consistency

Deployment, DevSecOps & Infrastructure

·        Implement secure DevSecOps pipelines for AI applications

·        Deploying solutions in AWS cloud environments

·        Build and manage infrastructure using IaC tools

·        Work with containerized applications (Docker/Kubernetes)

·        Ensure systems meet security and compliance requirements (especially federal environments)

General Responsibilities

·        Deliver production-grade, scalable AI agent solutions

·        Support Agile development practices and iterative delivery

·        Document architecture, workflows, and decisions

·        Stay current with advancements in Agentic AI and LLM technologies

Education:

·        Bachelor’s degree in Computer Science, Engineering, Data Science, Mathematics, or a related technical field

·        Master’s degree in scientific engineering or computer science preferred.

Required Qualifications:

·        1–2 years of hands-on experience implementing or deploying Agentic AI solutions

·        3+ years of Agile software development and DevSecOps experience

·        Experience building AI/ML-enabled systems, including LLM-based applications

·        Experience implementing Infrastructure as Code (IaC)

·        Strong programming skills in Python (primary); JavaScript (preferred)

·        Working knowledge of:

o   Linux environments

o   SQL and relational databases

o   Vector databases 

·         Must be able to obtain and maintain a High-Risk Public Trust or equivalent federal client access.   This role supports sensitive federal programs and involves elevated access to systems and data.

·         ·         Position requires the current ability to obtain and maintain required access without interruption in alignment with long-term program needs. 

·         Must be authorized to work in the United States without employer sponsorship now or in the future

Preferred Qualifications:

·        AWS Certification 

·        Experience deploying AI/ML solutions in federal or regulated environments

·        Experience with:

o   RAG architecture and retrieval optimization

o   Multi-agent systems and orchestration workflows

o   AI system evaluation and observability

About A P Ventures

A P Ventures is an Equal Employment Opportunity employer. All qualified applicants are considered without regard to race, national origin, gender, age, religion, disability, sexual orientation, veteran status, or marital status.