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-driven ...
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-driven ...
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 ...
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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 ...
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 ...
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 Fabric Architect
Phoenix, AZ · On-site
$63.25 - $81.50/hr
Familiarity with vector databases (e.g., Pinecone, Chroma, FAISS) for RAG-enabled systems * Exposure to AI orchestration frameworks such as LangChain or Semantic Kernel * Strong understanding of data ...
Data Fabric Architect
Phoenix, AZ · On-site
$63.25 - $81.50/hr
Familiarity with vector databases (e.g., Pinecone, Chroma, FAISS) for RAG-enabled systems * Exposure to AI orchestration frameworks such as LangChain or Semantic Kernel * Strong understanding of data ...
Java GENAI Engineer
Phoenix, AZ · On-site
$51.50 - $70.50/hr
LLM APIs (OpenAI / Gemini / Claude) • Prompt engineering Understanding of: • RAG (Retrieval-Augmented Generation) • Embeddings & vector databases Roles & Responsibilities • Experience with ...
New
Java GENAI Engineer
Phoenix, AZ · On-site
$51.50 - $70.50/hr
LLM APIs (OpenAI / Gemini / Claude) • Prompt engineering Understanding of: • RAG (Retrieval-Augmented Generation) • Embeddings & vector databases Roles & Responsibilities • Experience with ...
New
Data Fabric Architect
Phoenix, AZ · On-site
$63.25 - $81.50/hr
Familiarity with vector databases (e.g., Pinecone, Chroma, FAISS) for RAG-enabled systems * Exposure to AI orchestration frameworks such as LangChain or Semantic Kernel * Strong understanding of data ...
Data Fabric Architect
Phoenix, AZ · On-site
$63.25 - $81.50/hr
Familiarity with vector databases (e.g., Pinecone, Chroma, FAISS) for RAG-enabled systems * Exposure to AI orchestration frameworks such as LangChain or Semantic Kernel * Strong understanding of data ...
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
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
Java GENAI Engineer
Phoenix, AZ · On-site
$100K - $130K/yr
LLM APIs (OpenAI / Gemini / Claude) • Prompt engineering Understanding of: • RAG (Retrieval-Augmented Generation) • Embeddings & vector databases Roles & Responsibilities • Experience with ...
Java GENAI Engineer
Phoenix, AZ · On-site
$100K - $130K/yr
LLM APIs (OpenAI / Gemini / Claude) • Prompt engineering Understanding of: • RAG (Retrieval-Augmented Generation) • Embeddings & vector databases Roles & Responsibilities • Experience with ...
Java GENAI Engineer
Phoenix, AZ · On-site
$51.50 - $70.50/hr
LLM APIs (OpenAI / Gemini / Claude) • Prompt engineering Understanding of: • RAG (Retrieval-Augmented Generation) • Embeddings & vector databases Roles & Responsibilities • Experience with ...
New
Java GENAI Engineer
Phoenix, AZ · On-site
$51.50 - $70.50/hr
LLM APIs (OpenAI / Gemini / Claude) • Prompt engineering Understanding of: • RAG (Retrieval-Augmented Generation) • Embeddings & vector databases Roles & Responsibilities • Experience with ...
New
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 ...
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 ...
Experience with LangChain, OpenAI APIs, RAG, Vector Databases, or AI Agent frameworks * Familiarity with Docker, Kubernetes, Kafka, and CI/CD pipelines
Experience with LangChain, OpenAI APIs, RAG, Vector Databases, or AI Agent frameworks * Familiarity with Docker, Kubernetes, Kafka, and CI/CD pipelines
Java with Kotlin
Phoenix, AZ · On-site
AI Ecosystem Knowledge Awareness of modern AI concepts including LLMs, RAG, AI Agents, and vector databases Ability to integrate AI capabilities into enterprise applications securely and responsibly ...
Java with Kotlin
Phoenix, AZ · On-site
AI Ecosystem Knowledge Awareness of modern AI concepts including LLMs, RAG, AI Agents, and vector databases Ability to integrate AI capabilities into enterprise applications securely and responsibly ...
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 ...
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 ...
Engineer II Premium
Phoenix, AZ · On-site
$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 ...
Engineer II Premium
Phoenix, AZ · On-site
$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 ...
AI Platform Architect
Gilbert, 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 ...
AI Platform Architect
Gilbert, 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 ...
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 ...
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 ...
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 ...
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?
| Aspect | Vector Databases | Data Engineers |
|---|---|---|
| Required Skills | Database management, data modeling, query optimization | Data pipeline development, ETL processes, programming |
| Work Environment | Data storage systems, AI/ML projects, cloud platforms | Data infrastructure, cloud environments, big data tools |
| Industry Usage | AI, machine learning, recommendation systems | Data 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 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:
Job description
Job Title : AI Developer (Platform + Backend)
Location :Tempe ,AZ
Summary:
Photon is seeking a skilled AI Developer (Platform + Backend) to support the design, development, and implementation of AI-powered employee support platform.
The enterprise AI platform built to streamline support for frontline and HQ teams through intelligent automation, workflow orchestration, and AI-driven resolution experiences. The platform combines Generative AI, enterprise integrations, backend services, and workflow automation to improve operational efficiency and employee productivity.
The AI Developer will be responsible for building scalable backend services using Python and FastAPI, integrating AI capabilities, developing APIs, and supporting AI orchestration workflows across the platform. The role requires strong hands-on experience in backend engineering, cloud-native development, API integrations, and modern AI application development.
Responsibilities
Design, develop, and maintain scalable backend services using Python and FastAPI.
Build and integrate AI-powered services, APIs, and workflow orchestration components.
Develop RESTful APIs and microservices for frontend, mobile, and enterprise integrations.
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-driven decision flows.
Work with enterprise systems including HR, IT, identity, and operational platforms.
Develop secure, scalable, and high-performance backend architectures.
Implement asynchronous processing, event-driven communication, and API integrations.
Collaborate with frontend, mobile, DevOps, product, and AI engineering teams.
Support cloud-native deployments using containers and Kubernetes.
Write clean, maintainable, and testable code with proper documentation and testing practices.
Participate in code reviews, debugging, monitoring, and production support activities.
Contribute to CI/CD pipelines, automated testing, and engineering best practices.
Implement logging and monitoring for AI workflows including request tracing, latency tracking, and error analysis.
Qualifications:
5+ years of experience in backend or platform engineering.
Strong hands-on expertise in Python and FastAPI development.
Experience building REST APIs, microservices, and backend integration services.
Experience working with Generative AI, LLM APIs, AI orchestration frameworks, or AI-powered applications.
Strong understanding of distributed systems and cloud-native application development.
Experience with databases such as PostgreSQL, MongoDB, Redis, or vector databases.
Knowledge of asynchronous programming and event-driven architectures.
Experience with Docker, Kubernetes, CI/CD pipelines, and cloud platforms such as AWS, Azure, or Google Cloud Platform.
Strong understanding of API security, authentication, and authorization mechanisms.
Familiarity with Git-based workflows and Agile development practices.
Strong debugging, problem-solving, and communication skills.
Experience with RAG pipelines, vector databases, and semantic search.
Good to Have
Exposure to LangChain, LlamaIndex, OpenAI APIs, or similar AI frameworks.
Experience with Kafka, RabbitMQ, or messaging/event-streaming platforms.
Familiarity with Flutter/mobile integrations and frontend-backend communication.
Experience in QSR, retail, hospitality, or enterprise digital platforms.
Exposure to workflow automation and enterprise orchestration systems.
Experience with observability, monitoring, and logging tools.
Understanding of AI governance, prompt engineering, and AI evaluation frameworks.
AI Developer (Platform + Backend)@Tempe ,AZ . Please share your resumes to
5+ years of experience in backend or platform engineering.
Strong hands-on expertise in Python and FastAPI development.
Experience building REST APIs, microservices, and backend integration services.
Experience working with Generative AI, LLM APIs, AI orchestration frameworks, or AI-powered applications.
Strong understanding of distributed systems and cloud-native application development.
Experience with databases such as PostgreSQL, MongoDB, Redis, or vector databases.
About Photon
Sourced by ZipRecruiter
Company size
1 - 10 Employees
Headquarters location
Cambridge, MA, US
Year founded
1984