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Vector Databases Jobs in Arizona (NOW HIRING)

Staffingine LLC

AI Engineer

Phoenix, AZ · On-site

Develop and implement AI solutions using Python and AI frameworks such as Langgraph and Langchain Work with vector databases like Pinecone to manage and query highdimensional data Build and maintain ...

Photon

AI Devs (Platform + BE)_Tempe,AZ

Anthem, AZ · On-site

Integrate Generative AI services, LLMs, vector databases, and semantic search capabilities. * Build and implement agent-based workflows including multi-step execution, tool integrations, and AI ...

Photon

AI Devs (Platform + BE)_Tempe,AZ

Anthem, AZ · On-site

Integrate Generative AI services, LLMs, vector databases, and semantic search capabilities. * Build and implement agent-based workflows including multi-step execution, tool integrations, and AI ...

Photon

AI Dev (Features + CHQ)_Tempe,AZ

Anthem, AZ · On-site

Integrate Generative AI services, LLMs, vector databases, and semantic search capabilities. * Build and implement agent-based workflows including multi-step execution, tool integrations, and AI ...

Photon

AI Dev (Features + CHQ)_Tempe,AZ

Anthem, AZ · On-site

Integrate Generative AI services, LLMs, vector databases, and semantic search capabilities. * Build and implement agent-based workflows including multi-step execution, tool integrations, and AI ...

Omega Hires

Java AI Developer

Phoenix, AZ · On-site

$50.25 - $65/hr

Candidates should have practical experience building GenAI applications using LLMs, Retrieval-Augmented Generation (RAG), vector databases, prompt engineering, AI agents, and enterprise AI governance.

OmegaHires

Senior Java Backend Developer - GenAI

Phoenix, AZ · On-site

$119K - $155K/yr

Candidates should have practical experience building GenAI applications using LLMs, Retrieval-Augmented Generation (RAG), vector databases, prompt engineering, AI agents, and enterprise AI governance.

OmegaHires

Java Ai Architect

Phoenix, AZ · On-site

$62.50 - $84.50/hr

Experience with RAG, Prompt Engineering, AI Agents, and Vector Databases (Pinecone, Milvus, ChromaDB, FAISS).Strong knowledge of REST APIs, GraphQL, Kafka, and event-driven architecture.Experience ...

IntraEdge

Engineer II Premium

Phoenix, AZ

$82K - $110K/yr

... Matplotlib - Vector databases (ChromaDB, FAISS, pgvector) - Cloud Deployment (AWS/GCP/Azure) - Docker - Git - AI/ML Skills: - Retrieval-Augmented Generation (RAG) - Prompt engineering and ...

ClifyX

Java-Python-Senior Engineer

Phoenix, AZ · On-site

... vector databases, and observability systems for adaptive agent behavior. • Ensure reliability, performance, and maintainability through rigorous testing, type safety (mypy/pydantic), and production ...

Troon

AI Platform Architect

Scottsdale, AZ

Experience designing RAG pipelines and working with vector databases at production scale * Experience implementing agentic workflows or function-calling integrations with LLMs * Experience working ...

Troon Golf

AI Platform Architect

Scottsdale, AZ · On-site

Experience designing RAG pipelines and working with vector databases at production scale * Experience implementing agentic workflows or function-calling integrations with LLMs * Experience working ...

Honeywell

Sr. Advanced AI Software Engineer

Phoenix, AZ · On-site

$121K - $160K/yr

Embeddings, vector databases, and semantic search. * Programming & Software Engineering: Strong proficiency in Python (primary AI development language), Experience with at least one additional ...

Honeywell

Sr. Advanced AI Software Engineer

Phoenix, AZ · On-site

$121K - $160K/yr

Embeddings, vector databases, and semantic search. * Programming & Software Engineering: Strong proficiency in Python (primary AI development language), Experience with at least one additional ...

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All JobsVector Databases JobsVector Databases Jobs in Arizona

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.
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What cities in Arizona are hiring for Vector Databases jobs? Cities in Arizona with the most Vector Databases job openings:
Vector Databases jobs near you

AI Engineer

Staffingine LLC

Phoenix, AZ • On-site

Contractor

Re-posted 12 days ago

Job description

Job Title: AI Engineer
Job Location: Phoenix, AZ
Job Type: Contract

Job Description:

  1. Develop and implement AI solutions using Python and AI frameworks such as Langgraph and Langchain Work with vector databases like Pinecone to manage and query highdimensional data Build and maintain machine learning pipelines for data processing and model deployment Utilize ML frameworks including XGBoost and PyTorch for model development and training Design and optimize data processing workflows using SQL and other pipeline tools Integrate and manage APIs to support AI applications and services Collaborate with crossfunctional teams to deliver scalable AIdriven products 

Roles and Responsibilities 

  1. Design develop and deploy AI and machine learning models using Python and relevant AI stacks Manage vector databases to enhance data retrieval and storage efficiency Build robust ML pipelines to automate data ingestion preprocessing and model training Apply advanced ML frameworks such as XGBoost and PyTorch for predictive analytics Develop data processing solutions involving SQL and pipeline orchestration Create and maintain APIs for seamless integration of AI components Collaborate with data scientists engineers and stakeholders to ensure project success Continuously monitor and improve AI system performance and scalability

Skills

Mandatory Skills : Agentic Framework