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Pinecone Vector Databases Jobs in Seattle, WA (NOW HIRING)

Spatium Lab LLC

AI Engineer Intern

Bellevue, WA · On-site

$82.66 - $165.31/hr

Knowledge of vector databases (Pinecone, Weaviate, Chroma, pgvector) * Familiarity with AWS or GCP cloud services and containerization (Docker) * Understanding of security principles for multi-tenant ...

Accenture

Knowledge Engineer - Back End

Seattle, WA · On-site

Minimum 3 years experience deploying and operating vector databases (e.g., Pinecone, Weaviate, Milvus, or Qdrant) in production environments. * Minimum 3 years Proficiency in Python or Java for ...

Accenture

Knowledge Engineer - Back End

Seattle, WA

Minimum 3 years experience deploying and operating vector databases (e.g., Pinecone, Weaviate, Milvus, or Qdrant) in production environments. * Minimum 3 years Proficiency in Python or Java for ...

Tata Consultancy Services

AI Enterprise Architect

Seattle, WA · On-site

$78.50 - $101.25/hr

... Vector DBs (ChromaDB, Pinecone, FAISS, Weaviate, Vertex AI Matching Engine). • Develop and optimize retrieval-augmented generation (RAG) pipelines using vector databases • Define and enforce AI ...

Chewy

Software Engineer III

Bellevue, WA · On-site

$136K - $225K/yr

Understanding of LLM architectures, embeddings, and vector databases (e.g., Qdrant, Pinecone, Milvus, FAISS). * Demonstrated ability to drive cross-team technical initiatives and influence ...

Chewy

Software Engineer III

Bellevue, WA · On-site +1

$136K - $225K/yr

Understanding of LLM architectures, embeddings, and vector databases (e.g., Qdrant, Pinecone, Milvus, FAISS). * Demonstrated ability to drive cross-team technical initiatives and influence ...

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 ...

United Airlines

AI Architect/Developer

Seattle, WA · Hybrid

$82K - $193K/yr

Experience with Vector Databases (e.g., Pinecone, Milvus, Weaviate) and traditional RDBMS/NoSQL databases. The base compensation range for this role in the posted location is: $82,082 - $193,440.

United Airlines

AI Architect

Seattle, WA · Hybrid

$82K - $193K/yr

Experience with Vector Databases (e.g., Pinecone, Milvus, Weaviate) and traditional RDBMS/NoSQL databases. The base compensation range for this role in the posted location is: $82,082 - $193,440/year ...

Capgemini Engineering

AI Architect

Seattle, WA · On-site

$82K - $193K/yr

Experience with Vector Databases (e.g., Pinecone, Milvus, Weaviate) and traditional RDBMS/NoSQL databases. The base compensation range for this role in the posted location is: $82,082 - $193,440/year ...

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Pinecone Vector Databases information

What is a Pinecone vector database?

A Pinecone Vector Database is a cloud-based service designed to efficiently store, index, and search high-dimensional vector data, such as embeddings generated by machine learning models. It enables fast similarity search, making it ideal for use cases like semantic search, recommendation systems, and AI-powered applications. Pinecone handles the complexity of scaling and managing vector data, so developers can focus on building intelligent applications without worrying about infrastructure.

What are the key skills and qualifications needed to thrive as a Pinecone vector database engineer, and why are they important?

To thrive as a Pinecone Vector Database Engineer, you need a strong background in computer science, data engineering, and experience with large-scale distributed systems, often supported by a relevant degree or equivalent experience. Proficiency in Python, REST APIs, cloud platforms (AWS, GCP), and vector search technologies, along with familiarity with Pinecone’s SDK and database management, are commonly required. Strong analytical thinking, problem-solving abilities, and effective communication skills help you collaborate with cross-functional teams and deliver scalable solutions. These skills ensure robust database performance, efficient data retrieval, and successful integration of vector search capabilities into real-world applications.

What are some common challenges faced by engineers working with Pinecone vector databases, and how can they be addressed?

Engineers working with Pinecone Vector Databases often encounter challenges such as optimizing vector search performance at scale, ensuring data consistency across distributed systems, and integrating the database with various machine learning pipelines. Addressing these challenges typically involves tuning indexing parameters, monitoring resource utilization, and collaborating closely with data scientists to understand retrieval requirements. Regularly reviewing documentation and participating in community forums can also help engineers stay current with best practices and new features.

What is the difference between Pinecone Vector Databases vs Data Engineers?

AspectPinecone Vector DatabasesData Engineers
Primary RoleManaging and deploying vector database solutions for AI/ML applicationsDesigning, building, and maintaining data pipelines and infrastructure
Skills & CertificationsKnowledge of vector databases, cloud platforms, programming (Python, SQL)Data modeling, ETL processes, cloud services, programming (Python, Java)
Work EnvironmentTech companies, AI startups, cloud providersData-driven organizations, tech firms, finance, healthcare

While Pinecone Vector Databases specialists focus on deploying and managing vector database solutions for AI applications, Data Engineers build and maintain the data infrastructure that supports these systems. Both roles require programming skills and familiarity with cloud platforms, but their core responsibilities differ: one centers on database management, the other on data pipeline development.

What are popular job titles related to Pinecone Vector Databases jobs in Seattle, WA?

For Pinecone Vector Databases jobs in Seattle, WA, the most frequently searched job titles are:

What job categories do people searching Pinecone Vector Databases jobs in Seattle, WA look for?

The top searched job categories for Pinecone Vector Databases jobs in Seattle, WA are:

What cities near Seattle, WA are hiring for Pinecone Vector Databases jobs?

Cities near Seattle, WA with the most Pinecone Vector Databases job openings:

Pinecone Vector Databases jobs near you
Infographic showing various Pinecone Vector Databases job openings in Seattle, WA as of August 2026, with employment types broken down into 87% Full Time, 5% Part Time, 1% Temporary, and 7% Contract. Highlights an 84% Physical, 5% Hybrid, and 11% Remote job distribution.

AI Engineer Intern

Bellevue, WA • On-site

Spatium Lab LLC
See more jobs at Spatium Lab LLCexternal

$82.66 - $165.31/hr

Other

Posted 12 days ago

Job description

RAG, LLM Integration, Function Calling, AWS Bedrock, MCP/A2A

Build AI-powered features for B2B SaaS that handle real customer data securely. This is practical AI engineering, not research—you'll ship production systems that integrate LLMs, implement RAG pipelines, and build agent workflows. We're looking for engineers who understand that AI in production means thinking about security, multi-tenancy, costs, and reliability, not just prompts.

  • Design and implement RAG pipelines for engineering documentation and knowledge bases
  • Build function/tool calling systems with structured outputs and reliable execution
  • Integrate LLMs via AWS Bedrock, GCP Vertex AI, or OpenAI APIs
  • Develop MCP (Model Context Protocol) servers and A2A (Agent-to-Agent) workflows
  • Implement secure, multi-tenant AI features that protect customer data isolation
  • Build background task pipelines (Celery, SQS) for async AI processing at scale
  • Create embeddings pipelines and manage vector databases for semantic search
  • Monitor AI system performance, costs, and quality metrics in production
Requirements
  • Strong Python skills with async programming experience
  • Understanding of transformer architectures and LLM fundamentals—how they work, not just how to call them
  • Experience building with LangChain, LlamaIndex, or similar orchestration frameworks
  • Knowledge of vector databases (Pinecone, Weaviate, Chroma, pgvector)
  • Familiarity with AWS or GCP cloud services and containerization (Docker)
  • Understanding of security principles for multi-tenant systems—data isolation matters
  • Experience with message queues and background processing (Celery, SQS, Redis)
  • Self-directed problem solver who designs solutions, not just implements specs
  • Proficient with AI coding tools and prompt engineering—you've built with these, not just experimented
  • Prior professional experience not required if portfolio demonstrates real AI engineering capability
  • Competitive hourly compensation
  • Flexible schedule (full-time summer or part-time year-round)
  • Direct mentorship from founders and senior engineers
  • Real project ownership with production impact
  • Path to full-time conversion for exceptional performers
  • Modern tech stack and cutting-edge AI tools
  • Small team, high autonomy environment
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