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Vector Databases Jobs in Redmond, WA (NOW HIRING)

Uw

AI Agent Builder

Seattle, WA · On-site

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 ...

Microsoft

Software Engineer II - CoreAI

Redmond, WA · On-site

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 ...

Redapt

GenAI Consultant

Woodinville, WA

Familiarity with vector databases and embeddings * Exposure to Amazon Lex, Amazon Connect, or conversational AI platforms * AWS Certifications (Solutions Architect, AI Practitioner, or Developer ...

Provn

Full Stack Engineer

Seattle, WA

$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

Redapt

GenAI Consultant

Woodinville, WA · On-site

Familiarity with vector databases and embeddings * Exposure to Amazon Lex, Amazon Connect, or conversational AI platforms * AWS Certifications (Solutions Architect, AI Practitioner, or Developer ...

Trunk Tools

Data Scientist/BI Data Analyst

Redmond, WA · On-site

$80K/yr

... by vector databases. Our zero-distance innovation solutions for GenAI can reduce GenAI costs by up to 80% and bring solutions to market 50% faster. Our mission is to bridge the gap between AI ...

Databricks

Systems PhD - Software Engineer

Bellevue, WA

$140K - $180K/yr

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 ...

Databricks

Systems PhD - Software Engineer

Bellevue, WA · On-site

$140K - $180K/yr

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 ...

Tekwissen

Sr AI Platform Engineer

Bellevue, WA

$117K - $162K/yr

Vector databases and knowledge graphs: Pinecone, Weaviate, pgvector, Neo4j, or comparable; embeddings and retrieval patterns. * AI software engineering: hands-on building data infrastructure for AI ...

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All JobsVector Databases JobsVector Databases Jobs in Redmond, WA

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 job categories do people searching Vector Databases jobs in Redmond, WA look for? The top searched job categories for Vector Databases jobs in Redmond, WA are:
What cities near Redmond, WA are hiring for Vector Databases jobs? Cities near Redmond, WA with the most Vector Databases job openings:
Vector Databases jobs near you
Infographic showing various Vector Databases job openings in Redmond, WA as of June 2026, with employment types broken down into 87% Full Time, 10% Part Time, and 3% Contract. Highlights an 66% Physical, 4% Hybrid, and 30% Remote job distribution.

TECHNICAL ARCHITECT - CLOUD NATIVE & AI-LED ENGINEERING

UnivEdge Consulting LLC

Seattle, WA

$75.50 - $91/hr

Other

Posted 8 days ago

Job description

ROLE: TECHNICAL ARCHITECT – CLOUD NATIVE & AI-LED ENGINEERING

LOCATION: SEATTLE WA/ VANCOUVER BC

 

Position: We are looking for a Technology Partner who can combine technology leadership with strong execution ownership in a modern cloudnative and AI-led engineering environment.

This role requires a strong builder mindset — leaders who think like product companies, not traditional services organizations.

We are looking for someone who can drive engineering excellence, platform thinking, automation-first execution, and outcome ownership at scale.

 

Key Responsibilities

• Design scalable cloud-native technology landscapes across platforms, data, and integrations.

• Infuse Co-Pilot / AI into SDLC to improve engineering productivity, quality, and release velocity.

• Drive spec-driven development: o Requirements → Specs → User Stories → Test Cases → Code → Deployment.

• Push PDMs, architects, and engineering teams toward stronger delivery discipline and accountability.

• Establish strong engineering fundamentals: o Design validation, Code review rigor, CI/CD discipline, Automated testing, Release governance

• Own delivery outcomes end-to-end: o Quality, Timelines, Release readiness, Customer confidence

• Drive automation-first execution and reduce dependency on manual SDLC processes.

• Operate with high ownership, urgency, and bias for action.

 

Technical Expectations Strong understanding of modern engineering ecosystem including:

Languages & Frameworks: Python, Java, JavaScript / TypeScript, Node.js, React, SQL, API-first and microservices architecture

Cloud & DevOps: AWS / Azure / Google Cloud Platform, Kubernetes, Docker, Terraform, GitHub Actions / Jenkins, Observability and monitoring platforms

 

AI-Led Engineering:

• GitHub Copilot / Cursor / AI-assisted coding tools

• LLM integration patterns

• AI-driven testing and code review workflows

• Agentic SDLC concepts Open Source & Platform Components: Awareness of modern open-source ecosystem including:

• PostgreSQL, Redis, Kafka, Airflow, dbt, Elastic / OpenSearch, Vector databases, LangChain / orchestration frameworks Open-source LLM ecosystem (Llama, Mistral, etc.)

 

What We Are Looking For

• Strong cloud-native architecture and engineering background.

• Experience driving AI-led SDLC and modern engineering practices.

• Product engineering / builder mindset preferred over traditional services management mindset.

• Ability to challenge teams constructively and raise the engineering bar.

• Strong ownership mentality with focus on execution and outcomes.

• Ability to operate effectively in fast-paced enterprise environments.

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