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Vector Databases Jobs in Maryland (NOW HIRING)

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 ...

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 ...

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 ...

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 ...

Vector Databases: Knowledge of vector databases and embedding systems * Distributed Systems: Experience with high-performance computing or distributed systems About BigBear.ai BigBear.ai is a leading ...

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 ...

Vector Databases: Knowledge of vector databases and embedding systems * Distributed Systems: Experience with high-performance computing or distributed systems About BigBear.ai BigBear.ai is a leading ...

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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:
AI/ML Security Integration Engineer with Security Clearance

AI/ML Security Integration Engineer with Security Clearance

BigBear.ai

Annapolis Junction, MD • On-site

Other

Re-posted yesterday


Job description

Overview BigBear.ai is seeking an AI/ML Security Integration Engineer with an active TS/SCI clearance with a Poly, to configuring, fine-tuning, and maintaining an ATO Automation Platform's Large Language Model (LLM) capabilities for secure operation within customer environments. This role ensures the AI components of the compliance automation platform function effectively while meeting stringent security requirements for federal deployments, including classified and air-gapped networks. This position will be based out of our Columbia, MD office but will support multiple customers in the Baltimore/Washington corridor and beyond. What you will do * Configure and optimize the ATO Automation Platform's LLM backends (e.g. GPT-4, Claude Sonnet, Llama) based on customer security requirements and deployment constraints * Implement and maintain the Retrieval-Augmented Generation (RAG) architecture that powers the ATO Automation Platform's automated compliance documentation * Develop and manage customer-specific knowledge bases incorporating organizational policies, security guides, and system documentation for AI processing * Fine-tune LLM parameters and prompt engineering to ensure outputs match organization-specific documentation standards and terminology * Implement secure data handling procedures for AI processing of potentially sensitive system configurations and security information * Configure vector databases and enterprise search technologies that support the ATO Automation Platform's compliance knowledge graph * Monitor AI model performance and accuracy of generated compliance artifacts, implementing continuous improvement processes * Deploy approved LLM models in restricted environments including air-gapped networks and classified systems * Deploy and configure open-source Llama model in air-gapped DoD environment for processing IL5 system configurations * Fine-tune Claude Sonnet prompts to generate control implementation descriptions matching agency-specific SSP format requirements * Build custom RAG knowledge base incorporating customer's 500+ security policies and standard operating procedures * Implement data sanitization pipelines to redact classified markings before AI processing * Optimize vector embedding strategies to improve the ATO Automation Platform's accuracy in mapping code configurations to NIST 800-171 controls What you need to have * Bachelor's Degree with a Technical concentration with at least 10 years of professional experience * TS/SCI with an active Poly clearance * All applicants must currently reside in the United States * Strong experience with Large Language Models and generative AI technologies (GPT-4, Claude, Llama) * Proficiency in implementing Retrieval-Augmented Generation (RAG) architectures * Experience with vector databases and semantic search technologies * Programming proficiency in Python for AI/ML workflows and automation * Understanding of prompt engineering and LLM fine-tuning techniques * Knowledge of secure AI/ML deployment practices and data handling * Experience with cloud-based AI services (AWS Bedrock, Azure OpenAI) and their government cloud implementations * Understanding of cybersecurity principles and secure software development What we'd like you to have * Experience deploying AI/ML models in classified or air-gapped environments * Knowledge of AI governance frameworks and ISO 42001 (AI management systems) * Familiarity with compliance automation use cases and security control frameworks * Experience with natural language processing (NLP) for technical documentation * Understanding of model evaluation metrics and AI accuracy assessment * Background in federal AI adoption requirements and responsible AI principles * Certifications: AWS Machine Learning Specialty, Azure AI Engineer, TensorFlow Developer * Experience with containerized AI deployments and MLOps practices About BigBear.ai BigBear.ai is a leading provider of AI-powered decision intelligence solutions for national security, supply chain management, and digital identity. Customers and partners rely on Bigbear.ai's predictive analytics capabilities in highly complex, distributed, mission-based operating environments. Headquartered in McLean, Virginia, BigBear.ai is a public company traded on the NYSE under the symbol BBAI. For more information, visit https://bigbear.ai/ and follow BigBear.ai on LinkedIn: @BigBear.ai and X: @BigBearai. BigBear.ai is an Equal opportunity employer all protected groups, including protected veterans and individuals with disabilities.