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Vector Databases Jobs in Maryland (NOW HIRING)

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

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

$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 ...

Leidos

Agentic AI Developer

Columbia, MD · On-site

$69K - $125K/yr

Familiarity with REST APIs, vector databases, and modern backend architectures. Strong understanding of prompt engineering, RAG pipelines, and agent orchestration concepts. Strong communication and ...

The Josef Group

Senior Data Scientist

Columbia, MD

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 ...

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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 Developer / Full Stack Developer Associate

AI Developer / Full Stack Developer Associate

LCG, Inc.

Bethesda, MD • Hybrid

Full-time

Medical, Dental, Vision, Life, Retirement, PTO

Posted 9 days ago

Job description


Job title: Full Stack Developer / AI Developer, Associate

Location: Bethesda, MD

Clearance: Public Trust

Sponsorship: No sponsorship assistance is available for this position.

Duration: July 2026 – December 2026

Hybrid: Minimum of 2 Days Onsite (May increase as Client needs may increase)

Job Overview: LCG is seeking a Full Stack Developer / AI Engineer – Associate to support our NIH client in developing innovative AI-powered solutions using Azure OpenAI, Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and modern full stack technologies.

This role will support an NIH client that aims to design and implement AI-driven applications that automate and enhance internal NIH business processes. The developer will design and build Generative AI applications, chatbots, and intelligent automation tools to support use cases such as compliance review, policy analysis, meeting scheduling, grant monitoring, and research reporting.

The successful candidate will support configuration and assist with optimizing secure Azure OpenAI cloud infrastructure, design LLM-integrated applications using Python-based APIs, and enhance the existing client AI Chat Tool to improve knowledge retrieval and operational efficiency. The role involves building React-based front-end interfaces, developing FastAPI services for AI integration, and implementing vector databases to support semantic search and RAG pipelines.

This role will work closely with client leadership, technical teams, and pilot users to prototype, deploy, and refine AI capabilities while ensuring alignment with federal IT security, governance, and change management processes.

This position offers an opportunity to contribute to cutting-edge AI modernization initiatives at NIH, helping federal programs leverage Generative AI technologies to improve efficiency, decision-making, and operational insights.

Key Responsibilities

AI Solutions Development

  • Develop and implement AI-powered applications using Azure OpenAI, LLM technologies, Retrieval-Augmented Generation (RAG) pipelines, and vector database architectures
  • Design and build Generative AI applications, intelligent agents, and chatbot solutions that automate internal business processes and support staff workflows.
  • Implement semantic search and document retrieval systems using vector databases to support AI-driven knowledge retrieval.
  • Enhance and maintain the existing client AI Chat Tool, improving user experience and response accuracy through AI technologies.
  • Develop intelligent Generative AI applications supporting use cases such as:
    • Compliance verification for new policies and funding opportunities
    • Compliance verification for new policies and funding opportunities
    • Policy and regulatory change analysis
    • AI-driven meeting scheduling and coordination
    • Monitoring of grant and clinical trial activities
    • Knowledge retrieval from internal documentation and SOP repositories

Cloud Engineering and AI Infrastructure

  • Support the configuration and enhancement of secure Azure cloud infrastructure used to host AI applications and services, including:
    • Azure OpenAI services
    • Azure Storage accounts
    • Azure Applications and Database services
  • Assist cloud and infrastructure teams with deploying AI-powered applications that leverage vector databases and RAG architectures.
  • Work within the existing Azure OpenAI environment to integrate AI services and ensure applications function effectively within the client’s cloud infrastructure.
  • Collaborate with cloud engineering and security teams to ensure AI solutions align with NIH cloud governance, security policies, and infrastructure standards.
  • Assist with documenting AI solution architecture and implementation components.

Full Stack Development and Integration

  • Develop full stack AI applications using React for front-end interfaces and Python-based APIs for backend services.
  • Build RESTful APIs and AI service endpoints using FastAPI to connect LLM services with enterprise applications.
  • Support development of RAG pipeline components integrating vector databases with enterprise data sources.
  • Assist in developing LLM-integrated applications and APIs that connect AI services with enterprise systems.
  • Implement data pipelines and integrations using SQL, NoSQL, and vector databases as well as external APIs.
  • Develop backend and automation services using Python, FastAPI, and modern API frameworks.
  • Utilize GitHub for version control, code collaboration, and maintaining source code repositories across development environments.

AI Use Case Development and Pilot Implementation

  • Collaborate with stakeholders to define, prototype, test, and deploy AI use cases.
  • Work with client staff to assess automation opportunities and evaluate operational efficiency improvements.
  • Support analysis of automation opportunities and document potential efficiency improvements
  • Assist with analyzing and documenting cloud resource usage and cost considerations for AI deployments.
  • Leverage Microsoft Power Automate to support workflow automation and integrate AI-powered processes into existing business applications.
  • Utilize Power BI to develop dashboards and reports that visualize application performance, usage metrics, operational insights for stakeholders.
  • Prepare and complete status reports, providing updates on development progress, milestones, risks, and pilot outcomes to client leadership and stakeholders.

Testing, Documentation, and Testing

  • Conduct User Acceptance Testing (UAT) with pilot users and incorporate feedback into system improvements.
  • Develop technical documentation, including:
  • Requirements documentation
  • Architecture and design documents
  • Testing plans and implementation strategies
  • Standard operating procedures (SOPs)
  • Create a fact sheets for Generative AI applications developed, summarizing functionality, key features, use cases, and benefit for stakeholders and end users.
  • Develop training materials and recorded training sessions to support user adoption.

Qualifications

Education – Bachelor’s degree from an accredited institution in related fields (Computer Science, Information Technology, Engineering, Mathematics, Data Science, Artificial Intelligence, etc)

Experience

Required:

  • Minimum 2 years of experience applying AI or machine learning to real-world technology solutions.
  • Minimum 2 years of experience working with Microsoft Azure Cloud and Azure OpenAI services.
  • Experience designing and implementing AI-powered applications using LLMs or Generative AI technologies.
  • Experience developing RAG pipelines, AI chatbots, or intelligent automation tools.
  • Strong programming skills in Python, with experience developing APIs using FastAPI or similar frameworks.
  • Experience building modern front-end interfaces using React or similar JavaScript frameworks.
  • Experience working with vector databases (Azure Databases) to support semantic search or AI retrieval workflows.
  • Experience with data engineering technologies including SQL, NoSQL, and API integrations.
  • Experience using GitHub for source code management, version control, pull requests, and collaborative development workflows.

Preferred:

  • Experience integrating LLM-based systems with enterprise applications and APIs.
  • Experience supporting federal IT environments (NIH or HHS preferred) (nice to have)
  • Experience implementing secure AI architectures in cloud environments.

Certifications (Preferred)

  • Microsoft Azure AI Engineer Associate
  • Microsoft Azure Developer Associate
  • Microsoft Azure Fundamentals (AZ-900)
  • ITIL 4
  • AI / Machine Learning certification
  • Cloud architecture or DevOps certification

Required Skills and Competencies

  • Strong analytical thinking and problem-solving abilities
  • Ability to translate complex technical concepts to non-technical stakeholders
  • Excellent written and verbal communication skills
  • Ability to manage multiple priorities in a fast-paced environment
  • High attention to detail and commitment to quality
  • Work independently, Self-motivated, proactive, and highly organized

Compensation and Benefits

The projected compensation range for this position is $90,000 to $110,000 per year benchmarked in the Washington, D.C. metropolitan area. The salary range provided is a good faith estimate representative of all experience levels. Salary at LCG is determined by various factors, including but not limited to role, location, the combination of education/training, knowledge, skills, competencies, certifications, and work experience.

LCG offers a competitive, comprehensive benefits package which includes health insurance options (medical, dental, vision), life and disability insurance, retirement plan contributions, as well as paid leave, federal holidays, professional development, and lifestyle benefits.

Devoted to Fair and Inclusive Practices

All qualified applicants will receive consideration for employment without regard to sex, race, ethnicity, age, national origin, citizenship, religion, physical or mental disability, medical condition, genetic information, pregnancy, family structure, marital status, ancestry, domestic partner status, sexual orientation, gender identity or expression, veteran or military status, or any other basis prohibited by law.

If you are interested in applying for employment with LCG and need special assistance or an accommodation to apply for a posted position, contact our Human Resources department by email at hr@lcginc.com.

Securing Your Data

Beware of fraudulent job offers using LCG's name. LCG will never request payment-related details or advancement of money during the application process. Legitimate communication will only come from lcginc.com or system@hirebridgemail.com emails, not free commercial services like Gmail or WhatsApp. If you receive suspicious emails asking for payment or personal information, contact us immediately at hr@lcginc.com.

If you believe you are the victim of a scam, contact your local law enforcement and report the incident to the U.S. Federal Trade Commission.