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Pinecone Vector Databases Jobs in California (NOW HIRING)

Apple

AI Data Engineer

Cupertino, CA

$207K - $311K/yr

  • Medical

  • Dental

  • Retirement

... with vector databases (Pinecone, Weaviate, Chroma), embedding generation pipelines, document stores (MongoDB or similar) and their integration patterns Understanding of RAG, MCP architectures ...

Ingram Micro

Software Engineer - AI

Irvine, CA · On-site

$98.60 - $157.80/hr

  • Medical

  • Life

  • Retirement

  • PTO

PostgreSQL, SQL databases, NoSQL databases, Vector Databases (pgvector, Pinecone, Weaviate, ChromaDB, etc.)* Exposure to one or more Cloud Platforms, such as: Google Cloud Platform (GCP), Amazon Web ...

ClinDCast LLC

Python Backend Engineer

Sunnyvale, CA · On-site

$60 - $65/hr

Experience with Vector Databases such as Pinecone, Weaviate, or similar technologies. Proficiency with Docker and cloud platforms (AWS or GCP). Experience with asynchronous programming and task ...

Ridgeline

Staff AI Data Platform Engineer

San Ramon, CA · On-site

  • PTO

Comfort with modern data platforms beyond standard relational databases - think Snowflake, vector databases (e.g., Pinecone, Weaviate, pgvector), and similar technologies. * Familiarity with data ...

Hop

AI/ML Engineer

San Francisco, CA · On-site

Responsibilities : • Build and maintain production-grade LLM pipelines and agentic workflows. • Design and optimize RAG architectures using vector databases (Pinecone, FAISS, Weaviate) at scale ...

Ridgeline

Staff AI Data Platform Engineer

San Ramon, CA · On-site

  • PTO

Comfort with modern data platforms beyond standard relational databases - think Snowflake, vector databases (e.g., Pinecone, Weaviate, pgvector), and similar technologies. * Familiarity with data ...

Everpure

Software Engineer

Santa Clara, CA

  • PTO

Familiarity with vector databases such as Pinecone, Weaviate, OpenSearch, or similar. * Experience with CI/CD pipelines and containerized deployments using Docker and Kubernetes. * Exposure to ...

Info Way Solutions

Golang

San Jose, CA · On-site

... vector databases such as pgvector, Pinecone, or Weaviate, and search APIs. • Solid knowledge of RESTful API design and distributed systems. • Demonstrated ability to work effectively as a team ...

Hop

AI/ML Engineer

San Francisco, CA

Design and optimize RAG architectures using vector databases (Pinecone, FAISS, Weaviate) at scale. * Implement agentic systems using LangGraph, LlamaIndex, or equivalent: tool use, multi-agent ...

Resource Logistics Inc.

Python Engineer

San Jose, CA · On-site

... Vector Databases (Pinecone, Weaviate) Company : Resource Logistics is an information technology company providing ERP, CRM implementation, software solutions, and staffing services. Founded in 1997 ...

Hop

AI/ML Engineer

San Francisco, CA · On-site

Design and optimize RAG architectures using vector databases (Pinecone, FAISS, Weaviate) at scale. Implement agentic systems using LangGraph, LlamaIndex, or equivalent: tool use, multi-agent ...

Plenful

Machine Learning Engineer

San Francisco, CA · On-site

  • Medical

  • Dental

  • Vision

  • Retirement

  • PTO

... vector database such as Pinecone, Weaviate, or Qdrant for RAG pipelines) * You've built data pipelines using SQL and distributed data processing tools * You're familiar with cloud platforms such as ...

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All JobsPinecone Vector Databases JobsPinecone Vector Databases Jobs in California

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 California?

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

What job categories do people searching Pinecone Vector Databases jobs in California look for?

The top searched job categories for Pinecone Vector Databases jobs in California are:

What cities in California are hiring for Pinecone Vector Databases jobs?

Cities in California with the most Pinecone Vector Databases job openings:

Pinecone Vector Databases jobs near you
Infographic showing various Pinecone Vector Databases job openings in California as of August 2026, with employment types broken down into 91% Full Time, 5% Part Time, and 4% Contract. Highlights an 81% Physical, 6% Hybrid, and 13% Remote job distribution.

Senior Agentic AI Engineer (Palo Alto Networks Ecosystem)

Staffingine LLC

Palo Alto, CA • On-site

$122K - $168K/yr

Contractor

Re-posted 15 days ago

Job description

Job Title: Senior Agentic AI Engineer (Palo Alto Networks Ecosystem) 
Job Location: Palo Alto , CA
Job Type: Contract

Job Description:

  1. Multi-Agent Orchestration: Design and deploy collaborative agent swarms using frameworks like LangGraph, CrewAI, or AutoGen to automate complex security remediation workflows. 
  2. Tool & Skill Engineering: Build "Agent Skills" and reusable toolkits that allow LLMs to interact securely with internal APIs, databases, and network protocols (BGP, IPsec, SD-WAN). 
  3. Universal Connectivity (MCP): Implement the Model Context Protocol (MCP) to create standardized, plug-and-play interfaces between agents and the PANW ecosystem. 
  4. Context Engineering & Budgeting: Manage the "Context Window" as a resource—optimizing token usage through advanced RAG (Retrieval-Augmented Generation) and semantic caching. 
  5. Sandboxed Execution: Develop Programmatic Tool Calling environments (e.g., Python sandboxes) where agents can execute code to filter and aggregate data before returning a final response. 
  6. Agentic Validation: Build "Evaluation LLM-as-a-Judge" frameworks to measure agent accuracy, latency, and tool-use reliability before production deployment. 

Technical Requirements: 

  1. Languages: Expert-level proficiency in Python or Go (for high-performance backend orchestration). 
  2. AI Frameworks: Deep experience with LangChain, LangGraph, LlamaIndex, and Semantic Kernel. 
  3. Model Context: Proven ability to implement MCP for secure agent-to-tool communication. 
  4. Infrastructure: Hands-on experience with Kubernetes, Docker, and cloud-native architectures (GCP/AWS). 
  5. Database Mastery: Experience with Vector databases (Pinecone, Milvus, Weaviate) and hybrid search strategies. 
  6. Cybersecurity DNA: Familiarity with SOAR (Security Orchestration, Automation, and Response) and SASE architectures is a massive plus.