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Vector Databases Jobs in California (NOW HIRING)

Build and implement RAG pipelines with vector databases (e.g., Pinecone, FAISS). Develop Generative AI solutions, including chatbots, summarization, and content creation tools. Preprocess, clean, and ...

Develop Retrieval Augmented Generation (RAG) solutions leveraging vector databases and enterprise knowledge stores. * Implement prompt engineering, context engineering, memory management, and multi ...

Develop Retrieval Augmented Generation (RAG) solutions leveraging vector databases and enterprise knowledge stores. * Implement prompt engineering, context engineering, memory management, and multi ...

Develop Retrieval Augmented Generation (RAG) solutions leveraging vector databases and enterprise knowledge stores. * Implement prompt engineering, context engineering, memory management, and multi ...

AI Engineer (Mid-Level)

San Francisco, CA · On-site

$180K - $400K/yr

Own production retrieval-augmented generation (RAG) pipelines and retrieval infrastructure, including vector databases, embeddings, and indexing for domain-specific search at scale. * Implement multi ...

AI Engineer (Mid-Level)

San Francisco, CA · On-site

$180K - $400K/yr

Own production retrieval-augmented generation (RAG) pipelines and retrieval infrastructure, including vector databases, embeddings, and indexing for domain-specific search at scale. * Implement multi ...

Contribute to model provider gateways, RAG pipelines, and vector database implementations that keep Ridgeline ahead of the curve in its industry. * Raise the bar. This is an uplevel hire - you'll be ...

... vector databases, search/retrieval systems, and external APIs. • Collaborate with product managers, AI researchers, data engineers, and UX teams to translate high-level agent use cases into robust ...

Showing results 41-60

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 in California are hiring for Vector Databases jobs? Cities in California with the most Vector Databases job openings:
Infographic showing various Vector Databases job openings in California as of August 2026, with employment types broken down into 100% Full Time. Highlights an 100% In-person job distribution.

Agentic AI Lead Engineer with OCR experience - W2 Role

Saransh Inc

Woodland Hills, CA • On-site

$108K - $142K/yr

Contractor

Re-posted 4 days ago


Job description

Job Title: Agentic AI Lead Engineer with OCR pipeline building experience
Location(s): Woodland Hills, CA / Atlanta, GA / Richmond, VA (Onsite from Day 1)
Job Type: Contract (W2)
 
Note: Need Profiles with OCR pipeline building experience
 
Skill Metrix:
Name
Required
GCP
Yes
Vector Databases
Yes
Large Language Model (LLM)
Yes
TypeScript
Yes
LLMs - Open Sources
Yes
Azure
Yes
AWS Cloud
Yes
Python
Yes
AI
Yes
Javascript
Yes

Top Skills:
• Strong programming experience in Python (required); familiarity with JavaScript/TypeScript is a plus
• Hands-on experience with LLMs, Generative AI, and agent frameworks (e.g., LangChain, LangGraph, AutoGen, CrewAI or similar)
• Experience building workflow automation solutions using APIs, event-driven systems, and microservices
Role Summary:
  • The Agentic AI Engineer is responsible for designing, building, and operationalizing agentic AI–driven automation solutions that autonomously plan, reason, and execute complex enterprise workflows.
  • This role focuses on leveraging multi-agent architectures, LLMs, orchestration frameworks, and enterprise integrations to automate business and IT processes with minimal human intervention, while ensuring security, governance, and Responsible AI compliance.
  • The engineer will work closely with enterprise architects, domain experts, platform teams, and business stakeholders to transform manual or rule-based workflows into intelligent, adaptive, agent-driven systems that deliver measurable business outcomes
Required Qualifications:
Technical Skills
• Knowledge of vector databases, knowledge graphs, and retrieval-augmented generation (RAG) patterns Familiarity with cloud platforms (Azure/AWS/GCP), containers, and infrastructure-as-code
Conceptual & Enterprise Skills
• Strong understanding of agentic AI concepts: autonomy, planning, reasoning, action, learning, and reflection.
• Experience translating business processes into automated, AI-driven workflows.
• Awareness of Responsible AI, security, and governance considerations in enterprise environments.
Preferred Qualifications:
• Experience implementing multi-agent orchestration in production environments.
• Background in enterprise platforms such as ERP, CRM, ServiceNow, or supply chain systems.
• Exposure to industry-specific agentic AI use cases (e.g., IT operations, SDLC automation, supply chain, customer service).
• Experience collaborating with architects, program leads, and domain experts in large-scale transformations.