1

Vector Databases Jobs in Washington, DC (NOW HIRING)

... vector databases, and advanced prompt‑engineering techniques. • Build agent frameworks supporting scientific discovery in areas like subsurface geology, supply chain intelligence, and materials ...

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

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

Agentic AI Developer

Chantilly, VA · On-site

$69K - $125K/yr

... vector databases, and modern backend architectures. • Strong understanding of prompt engineering, RAG pipelines, and agent orchestration concepts. • Strong communication and problem-solving ...

... vector databases, and advanced prompt‑engineering techniques. • Build agent frameworks supporting scientific discovery in areas like subsurface geology, supply chain intelligence, and materials ...

Architect and operationalize RAG pipelines, embeddings, vector databases, and LLM‑powered automation (chatbots, summarization, semantic search, anomaly detection). * Implement CI/CD pipelines ...

Architect and operationalize RAG pipelines, embeddings, vector databases, and LLM‑powered automation (chatbots, summarization, semantic search, anomaly detection). Implement CI/CD pipelines (GitHub ...

next page

Showing results 1-20

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.

Software & Data/AI Engineer with Security Clearance

Cornerstone Defense

Fairfax, VA • On-site

$117K - $140K/yr

Other

Medical, Dental, Vision, Life, Retirement, PTO

Posted 9 days ago


Job description

Title: Software & Data/AI Engineer Location: Tysons, VA *Clearance: *Active TS/SCI w/ Polygraph needed to apply * Company Overview: Cornerstone Defense is the Employer of Choice within the Intelligence, Defense, and Space communities of the U.S. Government. Realizing early on that our most prized assets are our employees, we continually focus our attention on improving the overall work/life experience they have supporting the mission.

Our Team is pushed every day to use their industry leading knowledge to provide end-to-end solutions to combat our nation's toughest and most secure problems. If you are looking for a place to not only be professionally challenged, but encouraged and supported by a company that cares, don't look any further than Cornerstone Defense. Benefits Overview : Cornerstone Defense offers a very comprehensive benefits package including, but not limited to: Medical, Dental and Vision Plans * Generous PTO Policy * 401(k) * HSA and FSA options * Life and Disability Insurance * Tuition Reimbursement and Training * Perks at Work Discount Program * Referral Program * Leads Generation Program * CollegeAmerica 529 * Fitness Reimbursement Program * Travel Assistance * Norton Lifelock Benefit Solutions * Life Planning Financial & Legal Services * Description: As a Software & Data/AI Engineer in this role, you will be tasked with the intricate process of analyzing both structured and unstructured datasets.

Your primary objective will be to identify commonalities across these datasets, organize them into a uniform structure, and perform entity resolution on the unstructured data. This will require a deep understanding of Python development and a proven track record of handling diverse data types. You will be expected to leverage your strong AI experience, particularly with large language models and GenAI, to implement a Chatbot-style interface that will serve as a critical touchpoint for stakeholders.

Your role will also involve the design, architecture, and prototyping of complex AI-based systems that are essential for the organizations data management strategy. Your technical toolkit should include proficiency in working with vector databases and SQL, as well as a solid grasp of User Interface implementation using frameworks such as React or Angular. Familiarity with AWS cloud architecture will be crucial as you will be responsible for deploying AI models that can process a wide array of structured and unstructured data formats.

The ability to clean data for ingestion and analysis is also a key part of your role, ensuring that the data is primed for optimal AI model performance. Your success in this position will be bolstered by your capacity to examine data holdings and use cases critically, enabling you to determine the most effective approach for grooming data for the AI systems you will be developing. To excel in this position, you should bring a wealth of experience in AI model optimization, which will be central to your daily responsibilities.

Your role will require you to integrate Large Language Model chatbots, a cutting-edge technology that will enhance the organizations data interaction capabilities. The ability to navigate and implement vector databases will be essential, as this forms the backbone of the data structuring process. Your success will be measured by your ability to seamlessly integrate AI models that effectively manage the organizations diverse data formats.

Your innovative approach to AI-based architecture will not only streamline data processes but also set new standards for data management within the organization. Here is what you need: * Python development * Handling structured and unstructured data * Entity resolution * Strong AI experience working with large language models and GenAI * Experience working with vector databases * SQL * User Interface implementation using Javascript, React, Angular or a similar framework * Experience integrating APIs * AWS cloud architecture * AI model optimization * Ability to examine data holdings and use cases to determine best approach for grooming data Bonus if you have: * Field specific skills or certifications related to AI, data analysis, or software engineering