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Vector Databases Jobs in Mesa, AZ (NOW HIRING)

IntraEdge

Architect

Phoenix, AZ · On-site

$63 - $83/hr

... and Vector Databases • Expertise in one or more AI frameworks, such as LangChain. • Knowledge of AI concepts, such as machine learning, deep learning, natural language processing ...

Arkhya Tech

GenAI Solutions Leader

Phoenix, AZ · On-site

Hands-on with GenAI and agentic AI (LLMs, diffusion models, RAG, tool use/agents); familiarity with OpenAI Azure, Hugging Face, LangChain/LangGraph, ADK, vector databases. Experience with MLOps ...

Perficientcorp Inc

Gen AI Engineer

Phoenix, AZ · On-site

Experience implementing RAG architectures, vector databases, and LLM lifecycle management (prompt engineering, context engineering, fine-tuning, evaluation, monitoring) * Strong programming skills in ...

Matthews

Staff Software Engineer (PHX)

Scottsdale, AZ · On-site

$170K - $220K/yr

Experience with LLMs, LangChain/LangGraph, and vector databases Salary Range - $170k-220k depending on capability level and industry experience svg]:px-3 text-sm tracking-[0.025rem] leading-[1.5rem ...

Tata Consultancy Service Limited

AI Engineer

Phoenix, AZ · On-site

$100K - $120K/yr

... with vector databases • Experience with cloud platforms (AWS, Azure AI, Google Cloud Vertex AI) and containerization technologies • Proven ownership of complex, cross-cutting agentic systems ...

MY DR NOW

AI & Machine Learning Engineer

Chandler, AZ · On-site

$100K - $110K/yr

RAG solutions and Vector Databases * Microsoft Fabric, Azure Data Factory, Azure SQL * TensorFlow, PyTorch, Scikit-learn, MLflow * Epic Clarity, Epic Caboodle, FHIR, HL7 * Claude Code, GitHub Copilot ...

Pwc

ERP AI Engineer - Senior Associate

Phoenix, AZ · On-site

$77K - $202K/yr

... with vector databases and embedding models - Track record of fine-tuning models on domain-specific data - Experience processing and managing data pipelines - Contributions to open-source AI/ML ...

Dutch Bros. Coffee

Lead Engineer, Data Platforms

Tempe, AZ · On-site

$99K - $131K/yr

Nice to have - Passion and drive for a POC / designing RAG architecture, vector databases, or integrating LLMs into data pipelines. * Familiarity with data privacy regulations knowledge (GDPR, CCPA ...

Lorven Technologies

AI Solution Architect

Tempe, AZ · On-site

$60.25 - $79.50/hr

Vector databases * Embeddings * Evaluation frameworks * Guardrails * Fine-tuning * Orchestration frameworks * 8+ years experience in Python. * Proficiency in C#, Java, or TypeScript is a plus. * 6+ ...

Dutchbros

Lead Engineer, Data Platforms

Tempe, AZ · On-site +1

$111K - $133K/yr

Nice to have - Passion and drive for a POC / designing RAG architecture, vector databases, or integrating LLMs into data pipelines. * Familiarity with data privacy regulations knowledge (GDPR, CCPA ...

TSMC

Senior Data Engineer / Data Curator

Phoenix, AZ · On-site

$130K - $177K/yr

Experience with vector databases and indexing for LLMs (e.g., FAISS, Pinecone). Interpersonal Skills: * Communication * Computer proficiency * Presentation skills * Listening * Teamwork Candidates ...

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Showing results 21-40

All JobsVector Databases JobsVector Databases Jobs in Mesa, AZ

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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Vector Databases jobs near you

AI Engineer with Security Clearance

Agensys Corporation

Phoenix, AZ • On-site

Other

Re-posted 20 days ago

Job description

Overview: We are looking for an AI/ML Engineer to develop, implement, and scale machine learning and generative AI solutions. This position blends strong software engineering expertise with practical experience building applications powered by modern AI frameworks and large language models. Required Skills Software Engineering * Proficient in Python and SQL; experience with Java or JavaScript is a plus * Basic front-end knowledge including HTML and CSS * Experience developing APIs using frameworks such as FastAPI * Familiarity with containerization and deployment using Docker Machine Learning Fundamentals * Hands-on experience developing and deploying machine learning models, including deep learning solutions * Strong understanding of model selection, evaluation techniques, and performance tuning * Experience operationalizing ML pipelines and monitoring model performance in production Frameworks & Tools * Practical experience working with LLM APIs and orchestration tools such as LangChain or LangGraph * Familiarity with platforms like Azure ML, Snowflake Cortex, and MLflow for managing the ML lifecycle Modern AI Concepts * Knowledge and experience with: * Vector databases and semantic search techniques * Retrieval-Augmented Generation (RAG) * Embeddings, chunking methods, and reranking strategies * Frameworks used to evaluate LLM performance * Tool usage and agent-based architectures * Optimizing cost and latency for AI-driven workloads Ideal Candidate * Demonstrates ownership across the full lifecycle, from initial prototype through production release * Comfortable building scalable AI-driven systems that deliver measurable business outcomes * Strong communicator who can clearly convey technical ideas in a business context * Candidates with a combination of strong engineering fundamentals and hands-on generative AI experience, especially with RAG architectures and production-level LLM applications-will be particularly competitive. Minimum Qualifications * Strong proficiency with Python, SQL, FastAPI, and Docker * Experience integrating LLM APIs and working with at least one orchestration framework (LangChain or LangGraph) * Exposure to enterprise AI platforms such as Azure ML, Snowflake Cortex, or MLflow

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