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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 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 ...

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 ...

GDH

AI Engineer

Phoenix, AZ · On-site

$60 - $67/hr

Use vector databases and semantic search techniques to improve retrieval quality and downstream results * Evaluate model and LLM performance using appropriate frameworks and testing approaches

Tata Consultancy Services

Java GENAI Engineer

Phoenix, AZ · On-site

$51.50 - $70.50/hr

... vector databases • Experience with Spring AI / LangChain4j • Vector DBs (Pinecone, OpenSearch, pgvector) • Knowledge of AI agents / orchestration frameworks • Cloud AI platforms (Azure AI ...

Tata Consultancy Services

Java GENAI Engineer

Phoenix, AZ · On-site

$51.50 - $70.50/hr

... vector databases • Experience with Spring AI / LangChain4j • Experience with Vector DBs (Pinecone, OpenSearch, pgvector) • Knowledge of AI agents / orchestration frameworks • Knowledge of ...

PLACE

AI Full-Stack Engineer

Scottsdale, AZ · On-site

$90K - $150K/yr

Work on creating and integrating AI agents that utilize large language models and vector databases to help agents move faster and smarter. * Scale & Deploy: Collaborate with our DevOps team to ensure ...

Intraedge

Senior AI Developer

Phoenix, AZ · On-site

$54 - $71.50/hr

Build and optimize document ingestion pipelines and document processing workflows utilizing vector databases (e.g., MongoDB Atlas). Manage and coordinate development tasks, collaborating with ...

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 ...

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 ...

Troon

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 ...

Intel

Analytics and AI Solution Architect

Phoenix, AZ

$188K - $265K/yr

... Vector databases in a Retrieval Augmented Generative AI architecture. - Apply Lambda architecture, database design, and data modeling to support scalable and efficient cloud-based applications ...

Honeywell

Sr. Advanced AI Software Engineer

Phoenix, AZ

$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 is the salary of a vector database developer?

The salary of a vector database developer typically ranges from $80,000 to $150,000 annually, depending on experience, location, and company size. Skilled developers with expertise in machine learning, data structures, and database management may earn higher salaries, especially in tech hubs or with advanced certifications.

Are vector databases the future?

Vector database jobs involve managing and optimizing databases designed for high-dimensional vector data, which are essential for AI and machine learning applications. As AI continues to grow, demand for professionals skilled in vector database technologies and related tools like embedding models is expected to increase, making this a promising field for future job opportunities.

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 can you do with a vector database?

A vector database is used in roles involving data management and machine learning to store, search, and retrieve high-dimensional vector representations of data such as images, text, or audio. It enables efficient similarity searches, supporting applications like recommendation systems, natural language processing, and computer vision. Working with a vector database often requires knowledge of data structures, indexing techniques, and programming skills in languages like Python or C++.

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 the top 5 vector databases?

Top vector databases used in data management and AI applications include Pinecone, Weaviate, FAISS, Milvus, and Annoy. These databases are optimized for storing and searching high-dimensional vector data, often requiring skills in machine learning and database management. They are widely adopted for tasks like similarity search and recommendation systems.
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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

AI Engineer

Staffingine LLC

Phoenix, AZ • On-site

Contractor

Re-posted 21 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