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Vector Databases Jobs in Phoenix, AZ (NOW HIRING)

Database Architect

Phoenix, AZ · On-site

$63.25 - $81.50/hr

Provide architectural guidance for AI-related data patterns including embeddings, vector databases, and RAG * Create and maintain architecture diagrams, technical documentation, and design decisions

Database Architect

Phoenix, AZ · On-site

$63.25 - $81.50/hr

Provide basic architectural guidance for AI-related data patterns , including embeddings, vector databases/vector search, and Retrieval-Augmented Generation (RAG). * Evaluate emerging database, cloud ...

Database Architect

Phoenix, AZ · On-site

$63.25 - $81.50/hr

Provide basic architectural guidance for AI-related data patterns, including embeddings, vector databases/vector search, and Retrieval-Augmented Generation (RAG). * Evaluate emerging database, cloud ...

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 ...

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 ...

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 ...

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 ...

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 ...

Data Engineer

Phoenix, AZ · On-site

$113K - $136K/yr

Support AI/ML and GenAI teams with data requirements, vector databases, embeddings, and RAG patterns * Create and maintain architecture diagrams, technical documentation, and design decisions

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.

Data Scientist II

Phoenix, AZ · On-site

$140K - $150K/yr

Rapidly prototype new AI use cases, including writing Python scripts that stand up vector databases and integrate LLMs * Take LLM applications from concept through production deployment * Perform ...

Experience implementing RAG (Retrieval-Augmented Generation) solutions using vector databases such as Pinecone, ChromaDB, Weaviate, Milvus, or FAISS . * Experience integrating AI models such as ...

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

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 ...

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

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

AI Platform Architect

Scottsdale, AZ · On-site

$120 - $150/hr

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

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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 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 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 popular job titles related to Vector Databases jobs in Phoenix, AZ?

For Vector Databases jobs in Phoenix, AZ, the most frequently searched job titles are:

What job categories do people searching Vector Databases jobs in Phoenix, AZ look for?

The top searched job categories for Vector Databases jobs in Phoenix, AZ are:

What cities near Phoenix, AZ are hiring for Vector Databases jobs?

Cities near Phoenix, AZ with the most Vector Databases job openings:

Infographic showing various Vector Databases job openings in Phoenix, AZ as of August 2026, with employment types broken down into 100% Full Time. Highlights an 50% In-person, and 50% Hybrid job distribution.

$63.25 - $81.50/hr

Full-time

This job post has expired today. Applications are no longer accepted.


Job description

Job Title: Database Architect
Location: Phoenix, AZ (Day 1 Onsite)
Job Type: Contract - Long Term
Experience: Senior-level with strong database architecture background
Job Overview
Our client is seeking an experienced Database Architect to design, optimize, and modernize scalable data platforms. The ideal candidate brings expertise in relational and NoSQL databases, cloud-based database services, data architecture, and performance optimization, along with working knowledge of Python and AI/ML technologies. This role partners with application, data engineering, cloud, security, and AI teams to define database architecture standards and build reliable, secure, and scalable data solutions.
Key Responsibilities
  • Design and define enterprise-level database architectures, standards, patterns, and best practices
  • Evaluate and select relational, NoSQL, and cloud-native database technologies based on business and technical requirements
  • Design scalable solutions covering data modeling, availability, resiliency, backup/recovery, replication, and disaster recovery
  • Optimize database performance through query tuning, indexing, partitioning, and capacity planning
  • Establish database security standards including encryption, access controls, auditing, and compliance
  • Use Python for database automation, scripting, data processing, and validation
  • Provide architectural guidance for AI-related data patterns including embeddings, vector databases, and RAG
  • Create and maintain architecture diagrams, technical documentation, and design decisions
  • Mentor and provide technical leadership to engineering teams
Required Skills
  • Strong experience in database architecture, engineering, or administration
  • Deep expertise in relational database concepts: SQL, data modeling, normalization, indexing, transactions, performance optimization
  • Hands-on experience with one or more: PostgreSQL, Oracle, SQL Server, MySQL, MongoDB
  • Hands-on experience with AWS, Azure, or GCP managed database services
  • Experience with database migration and modernization initiatives
  • Basic to intermediate Python skills for scripting and automation
  • Understanding of AI, ML, and Generative AI concepts
Preferred Skills
  • Experience with cloud-native and distributed database architectures
  • Infrastructure as Code and DevOps/CI/CD practices
  • Database observability, monitoring, and automated operations
  • Data governance, security, privacy, and regulatory compliance experience
  • Exposure to AI/ML platforms and cloud-based AI services
  • Experience with vector databases, embeddings, semantic search, LLMs, and RAG
  • Background supporting large-scale, high-volume enterprise systems
Location & Work Model
Phoenix, AZ - Day 1 Onsite
Engagement Details