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Vector Databases Jobs in Pleasanton, CA (NOW HIRING)

Intellectt INC

AI Software Engineer

Foster City, CA · On-site

RAG Architecture & Vector Databases * AI Agents & Conversational AI * LangChain / LlamaIndex / AutoGen * Backend & API Development * Cloud Technologies (AWS/GCP/Azure) * Docker / Kubernetes ...

Ridgeline

Staff AI Data Platform Engineer

San Ramon, CA · On-site

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 ...

DPP Tech, Inc.

Sr. Java Backend Engineer

Pleasanton, CA · On-site

$133K - $173K/yr

Experience with vector databases (Pinecone, Weaviate, Milvus, pgvector). * Knowledge of Python for AI workflows. * Experience with MCP (Model Context Protocol) or AI agents. Please share your updated ...

Addison Group

AI Architect

Alameda, CA · On-site

$115 - $135/hr

Design and implement Generative AI solutions including RAG pipelines, vector databases, and AI agents * Establish best practices for AI security, governance, and observability * Evaluate emerging AI ...

Addison Group

AI Architect

Alameda, CA · On-site

$115 - $135/hr

... vector databases, and AI agents • Establish best practices for AI security, governance, and observability • Evaluate emerging AI technologies and lead proof-of-concept initiatives through ...

Noblesoft Technologies

AI Developer

San Jose, CA · Remote

Integrate memory systems and RAG (Retrieval-Augmented Generation) using vector databases for context management. * Ensure agent reliability, safety, and governance by establishing robust guardrails ...

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Showing results 21-40

All JobsVector Databases JobsVector Databases Jobs in Pleasanton, CA

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 are popular job titles related to Vector Databases jobs in Pleasanton, CA? For Vector Databases jobs in Pleasanton, CA, the most frequently searched job titles are:
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What cities near Pleasanton, CA are hiring for Vector Databases jobs? Cities near Pleasanton, CA with the most Vector Databases job openings:
Vector Databases jobs near you
Infographic showing various Vector Databases job openings in Pleasanton, CA as of July 2026, with employment types broken down into 66% Full Time, and 34% Contract. Highlights an 92% In-person, and 8% Remote job distribution.

Urgent requirement // Data Scientist// Bay area // F2F Interview Must

Alliance IT

San Jose, CA • On-site

Other

Posted 13 days ago

Job description

Job Description – Data Scientist (Agentic AI)

Location: San Jose, CA (Hybrid/Onsite)

F2F Interview Must

About the Role

We are seeking an experienced Data Scientist with expertise in Agentic AI, Generative AI, and Machine Learning to design and deploy intelligent AI systems capable of autonomous reasoning, planning, tool usage, and multi-step decision-making. The ideal candidate will have a strong foundation in data science and hands-on experience building AI agents powered by large language models (LLMs) that solve complex business problems at scale.

Key Responsibilities
  • Design, develop, and deploy Agentic AI solutions using LLMs and autonomous AI frameworks.
  • Build multi-agent systems capable of planning, reasoning, memory management, and tool orchestration.
  • Develop predictive models, recommendation systems, and advanced analytics solutions.
  • Fine-tune, evaluate, and optimize LLMs for enterprise use cases.
  • Implement Retrieval-Augmented Generation (RAG) pipelines using vector databases.
  • Develop AI workflows that integrate with enterprise applications through APIs and external tools.
  • Collaborate with Product, Engineering, and Business teams to identify AI opportunities and deliver production-ready solutions.
  • Design evaluation frameworks for AI agents, including reasoning quality, latency, accuracy, and safety.
  • Build scalable ML pipelines and support MLOps practices for model deployment and monitoring.
  • Stay current with advancements in Agentic AI, Generative AI, and machine learning research.
Required Qualifications
  • Bachelor''s or Master''s degree in Computer Science, Data Science, Statistics, Mathematics, AI, or a related field.
  • 4+ years of experience in Data Science or Machine Learning.
  • 2+ years of hands-on experience with Generative AI and LLM-based applications.
  • Strong programming skills in Python and SQL.
  • Experience with machine learning frameworks such as PyTorch, TensorFlow, or scikit-learn.
  • Experience building Agentic AI applications using frameworks such as LangGraph, LangChain, CrewAI, AutoGen, Semantic Kernel, or similar.
  • Strong understanding of prompt engineering, function/tool calling, structured outputs, and LLM evaluation.
  • Experience with vector databases such as Pinecone, Weaviate, Chroma, or Milvus.
  • Familiarity with RAG architectures and embedding models.
  • Experience working with OpenAI, Anthropic, Google Gemini, or other enterprise LLM APIs.
  • Excellent analytical, communication, and stakeholder management skills.

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