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Vector Databases Jobs in Seattle, WA (NOW HIRING)

Familiarity with vector databases (e.g., Pinecone, Weaviate, ChromaDB, pgvector) and embedding-based retrieval. Experience with REST APIs, cloud platforms (AWS, Azure, or GCP), and containerization ...

Background or practical experience in Information Retrieval, Vector Databases or Large Language Models for real-world applications. * Demonstrated ability to design and deliver fault-tolerant, high ...

Full Stack Engineer

Seattle, WA · On-site

$130K - $200K/yr

Experience building with AI and ML technologies including LLMs, vector databases, and coding agents * Track record of shipping customer-facing products * Strong product sense and design sensibility

To make it happen we're building multi-cloud systems at every corner of the data ecosystem, from query engines, vector databases, training pipelines, and storage systems, down to the infrastructure ...

Senior AI Engineer (*3-Year LTE)

Seattle, WA · On-site

$118K - $163K/yr

Preferred : • Experience with vector databases, knowledge graphs, or information architecture for AI applications. • Experience with fine-tuning, distillation, or other model adaptation ...

New

Implement vector databases and embedding strategies to power retrieval-augmented generation pipelines over internal privacy knowledge bases. * Ensure data quality, lineage, and governance standards ...

AI Engineer

Seattle, WA · On-site +1

$160K - $200K/yr

You are comfortable working with embeddings, vector databases, and semantic search, and you understand how these approaches differ from traditional search and indexing systems. LLM fundamentals You ...

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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.
What cities near Seattle, WA are hiring for Vector Databases jobs? Cities near Seattle, WA with the most Vector Databases job openings:
AI Agent Builder

AI Agent Builder

Uw

Seattle, WA • On-site

Full-time

Posted 6 days ago


University Of Washington rating

8.4

Company rating: 8.4 out of 10

Based on 85 frontline employees who took The Breakroom Quiz

74th of 534 rated colleges and universities


Job description

Job Description

UW Information Technology has an outstanding opportunity for AI Agent Builder to join their team.

About this Opportunity

Reporting to a Technology Manager in Infrastructure Service and AI Platforms, the AI Agent Builder will support the artificial intelligence (AI) initiatives at the university and its three campuses. This role is a pivotal role in shaping and implementing our AI strategy to transform UW into an AI-powered University. The AI Agent Builder is a core technical role within the AI Platforms team, focused on the design, development, deployment, and ongoing enhancement of AI-powered agents and intelligent automation solutions that serve the university. The AI Agent Builder Engineer role will work within Service Management and AI Platform team under the UW's IT infrastructure Umbrella that provides critical technology support to all three campuses, UW Medicine, and research operations around the world.

This is a full-time hybrid position with the expectation of being in the Seattle, U-District office a minimum of 3 days per week.

Key Responsibilities

[25%] AI Agent Design & Development

  • Demonstrated experience designing, building, and iterating on AI agents to improve performance, functionality, and user outcomes.

  • Architect and implement advanced RAG pipelines, including embedding strategies, vector search optimization, contextual window management, and hybrid retrieval techniques.

[25%] Administrative & Workflow Automation

  • Identify and automate repetitive administrative processes across departments using AI-powered workflows.

  • Integrate AI agents with university enterprise systems (e.g., SIS, LMS, HRIS, ERP, ticketing systems) via APIs and connectors.

[15%] Research Support

  • Build AI tools that assist researchers with literature review, data analysis, grant writing support, and knowledge synthesis.

  • Support advanced use cases involving long-context reasoning, structured data augmentation, and research corpus grounding.

[20%] Custom Tools for Faculty & Staff

  • Develop bespoke AI-powered tools tailored to departmental needs (e.g., document drafting assistants, scheduling agents, data query tools).

  • Implement agent orchestration frameworks such as CrewAI or equivalent enterprise-grade platforms; experience with nebulaONE or similar orchestration environments is highly desirable.

[15%] Platform & Operations

  • Monitor agent performance, usage analytics, and user feedback to continuously improve deployed solutions.

  • Implement guardrails, safety mechanisms, and evaluation frameworks to ensure responsible AI behavior.

Required Qualifications

To be considered for this opportunity your application must demonstrate you meet both the minimum qualifications and additional qualifications listed below. Equivalent education and/or experience may substitute for minimum qualifications except when there are legal requirements, such as a license, certification, and/or registration.

Minimum Qualifications

Bachelor's degree in Computer Science, Data Science, Software Engineering, or a related field or experience.
3 + years of professional software development experience demonstrating strong computer science fundamentals and API integration expertise, professional experience in software development.

Demonstrated experience in AI/ML or LLM-based applications.
Demonstrated portfolio of deployed AI agent solutions or automation tools (GitHub repositories, case studies, or equivalent evidence of hands-on work).
Demonstrated experience building AI agents, chatbots, or conversational AI systems using modern LLM frameworks.
Hands-on experience with LLM APIs (e.g., OpenAI, Anthropic, Google, or open-source models) and prompt engineering.
Strong understanding of retrieval-augmented generation (RAG), embeddings, vector search, and contextual grounding strategies.
Familiarity with vector databases (e.g., Pinecone, Weaviate, ChromaDB, pgvector) and embedding-based retrieval.
Experience with REST APIs, cloud platforms (AWS, Azure, or GCP), and containerization (Docker).
Strong problem-solving skills and ability to work both independently and collaboratively in a cross-functional teams.

Preferred Qualifications

  • Experience working in higher education, research institutions, or the public sector.

  • Demonstrated experience designing, building, and iterating on AI agents

  • Experience with enterprise AI orchestration platforms such as nebulaONE or comparable environments.

  • Experience with agent-to-agent (A2A) coordination models and advanced tool-use frameworks.

  • Experience with fine-tuning, evaluation, or alignment of language models.

  • Knowledge of data privacy regulations (FERPA, HIPAA) and responsible AI principles.

  • Experience building no-code/low-code tools or platforms for non-technical users.

  • Contributions to open-source AI/ML projects.

  • Experience with REST API and MCP integrations is highly desirable; familiarity with A2A integrations is a plus but not required.

Working Conditions

Work in an open office environment and contribute to collaborative teamwork focused on problem-solving.
Daily interactions with other team members, subject matter experts and stakeholders at all levels of the organization.
While the general working hours are within Monday through Friday, 8 a.m.-5 p.m. the AI Agent Builder Engineer will, on occasion, need to adjust hours to accommodate the business needs and deadlines.
Attend and occasionally present at conferences.

Compensation, Benefits and Position Details

Pay Range Minimum:

$87,624.00 annual

Pay Range Maximum:

$142,392.00 annual

Other Compensation:

-

Benefits:

For information about benefits for this position, visit https://www.washington.edu/jobs/benefits-for-uw-staff/

Shift:

First Shift (United States of America)

Temporary or Regular?

This is a regular position

FTE (Full-Time Equivalent):

100.00%

Union/Bargaining Unit:

Not Applicable

About the UW

Working at the University of Washington provides a unique opportunity to change lives - on our campuses, in our state and around the world.

UW employees bring their boundless energy, creative problem-solving skills and dedication to building stronger minds and a healthier world. In return, they enjoy outstanding benefits, opportunities for professional growth and the chance to work in an environment known for its diversity, intellectual excitement, artistic pursuits and natural beauty.

Our Commitment

The University of Washington is committed to fostering an inclusive, respectful and welcoming community for all. As an equal opportunity employer, the University considers applicants for employment without regard to race, color, creed, religion, national origin, citizenship, sex, pregnancy, age, marital status, sexual orientation, gender identity or expression, genetic information, disability, or veteran status consistent with UW Executive Order No. 81.

To request disability accommodation in the application process, contact the Disability Services Office at 206-543-6450 or dso@uw.edu.

Applicants considered for this position will be required to disclose if they are the subject of any substantiated findings or current investigations related to sexual misconduct at their current employment and past employment. Disclosure is required under Washington state law.


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