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Vector Databases Jobs in Arkansas (NOW HIRING)

AAIT Health

AI Engineer

Little Rock, AR · On-site

Experience with vector databases/search or building retrieval layers over relational and document stores. Knowledge of PHI de-identification, redaction, and safe data handling. Tech Stack Frontend:

Pwc

ERP AI Engineer - Manager

Fayetteville, AR · On-site

$99K - $232K/yr

... with vector databases and semantic search architectures - Translating complex business problems into AI solution designs - Contributing to business development and proposal writing - Cloud ...

Pwc

ERP AI Engineer - Manager

Little Rock, AR · On-site

$99K - $232K/yr

... with vector databases and semantic search architectures - Translating complex business problems into AI solution designs - Contributing to business development and proposal writing - Cloud ...

Tyson Foods

Senior IT Data Engineer (Onsite)

Springdale, AR · On-site

$93K - $127K/yr

Lead code reviews, design and deploy agentic AI architectures and multi-agent systems that automate data engineering workflows, including RAG systems, vector databases, and LLM-integrated platforms.

Walmart

Principal, Data Scientist

Bentonville, AR

$110K - $220K/yr

Hands-on experience developing GenAI solutions using Large Language Models (LLMs), prompt engineering, retrieval-augmented generation (RAG), Skills, vector databases, and agentic workflows.

Walmart

Principal, Data Scientist

Rogers, AR · On-site

$110K - $220K/yr

Hands-on experience developing GenAI solutions using Large Language Models (LLMs), prompt engineering, retrieval-augmented generation (RAG), Skills, vector databases, and agentic workflows.

Walmart

Principal, Data Scientist

Fayetteville, AR

$110K - $220K/yr

Hands-on experience developing GenAI solutions using Large Language Models (LLMs), prompt engineering, retrieval-augmented generation (RAG), Skills, vector databases, and agentic workflows.

Walmart

Principal, Data Scientist

Elkins, AR

$110K - $220K/yr

Hands-on experience developing GenAI solutions using Large Language Models (LLMs), prompt engineering, retrieval-augmented generation (RAG), Skills, vector databases, and agentic workflows.

Walmart

Principal, Data Scientist

Decatur, AR

$110K - $220K/yr

Hands-on experience developing GenAI solutions using Large Language Models (LLMs), prompt engineering, retrieval-augmented generation (RAG), Skills, vector databases, and agentic workflows.

Walmart

Principal, Data Scientist

Centerton, AR

$110K - $220K/yr

Hands-on experience developing GenAI solutions using Large Language Models (LLMs), prompt engineering, retrieval-augmented generation (RAG), Skills, vector databases, and agentic workflows.

Walmart

Principal, Data Scientist

Bella Vista, AR

$110K - $220K/yr

Hands-on experience developing GenAI solutions using Large Language Models (LLMs), prompt engineering, retrieval-augmented generation (RAG), Skills, vector databases, and agentic workflows.

Walmart

Principal, Data Scientist

Bentonville, AR · On-site

Hands-on experience developing GenAI solutions using Large Language Models (LLMs), prompt engineering, retrieval-augmented generation (RAG), Skills, vector databases, and agentic workflows.

Walmart

Principal, Data Scientist

Springdale, AR

$110K - $220K/yr

Hands-on experience developing GenAI solutions using Large Language Models (LLMs), prompt engineering, retrieval-augmented generation (RAG), Skills, vector databases, and agentic workflows.

Walmart

Principal, Data Scientist

Greenland, AR

$110K - $220K/yr

Hands-on experience developing GenAI solutions using Large Language Models (LLMs), prompt engineering, retrieval-augmented generation (RAG), Skills, vector databases, and agentic workflows.

Walmart

Principal, Data Scientist

Goshen, AR

$110K - $220K/yr

Hands-on experience developing GenAI solutions using Large Language Models (LLMs), prompt engineering, retrieval-augmented generation (RAG), Skills, vector databases, and agentic workflows.

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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 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.
What are popular job titles related to Vector Databases jobs in Arkansas? For Vector Databases jobs in Arkansas, the most frequently searched job titles are:
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What cities in Arkansas are hiring for Vector Databases jobs? Cities in Arkansas with the most Vector Databases job openings:
Vector Databases jobs near you
AI Engineer

AI Engineer

Pain Treatment Centers of America

Little Rock, AR • On-site

Full-time

Posted 27 days ago

Pain Treatment Centers Of America rating

7.6

Company rating: 7.6 out of 10

Based on 5 frontline employees who took The Breakroom Quiz

Job description

About Us
AAIT Health (Advanced Artificial Intelligence Technology Health) is building a modern, HIPAA-compliant Electronic Medical Records (EMR) platform. We're focused on turning today's best AI and LLM capabilities into reliable, secure, production-grade workflows embedded directly into the EMR experience, so clinicians and staff can work smarter, faster, and with less administrative burden.
What You'll Do
  • Build agentic AI systems that can execute multi-step workflows (e.g., chart review ? summarize ? recommend next actions ? draft documentation ? route tasks) with appropriate human oversight.

  • Design and implement tool-using LLM workflows (function calling / tools, retrieval, structured outputs, planner-executor patterns, and guardrails).

  • Integrate AI capabilities into our EMR via backend services and APIs (e.g., .NET Core services, MySQL, and modern frontend clients).

  • Implement retrieval-augmented generation (RAG) and clinical knowledge workflows that query patient context, incorporate medical reference content, and cite sources with traceability.

  • Engineer safety, privacy, and compliance into AI workflows, including PHI-safe processing, audit logs, role-based access, minimum-necessary data, prompt/data redaction, and secure storage.

  • Evaluate and improve quality using automated and human-in-the-loop evaluation (e.g., grounding, hallucination rates, task success, latency, and cost).

  • Deploy and operate AI services in production, including monitoring, rate limiting, fallbacks, caching, incident response, and model/provider switching.

  • Collaborate cross-functionally with product, clinical SMEs, security/compliance, and engineering to ship AI-powered features that users trust.

Role Details
  • Full-time position with presence in the office required.

  • Core schedule: Monday-Thursday 8:00 a.m. to 5:00 p.m. and Friday 8:00 a.m. to 12:00 p.m., with occasional work outside regular business hours as needed.

  • Travel may be required.

  • The position operates in a professional office environment and involves significant time writing, typing, speaking, listening, standing, sitting, walking, and reaching.

  • Operation of standard office equipment, non-CDL motor vehicles, mobile phones, and related technology is expected.

Requirements
You Might Be a Fit If
  • You have strong software engineering fundamentals and production experience (APIs, testing, debugging, performance).

  • You have hands-on experience building with LLMs (OpenAI/Anthropic/others), including tool/function calling, structured outputs, and retrieval-augmented generation (RAG).

  • You've integrated AI into real products (not just notebooks or demos) and understand the tradeoffs of latency, cost, and quality.

  • You can design for reliability with deterministic interfaces, schema validation, retries, fallbacks, and evaluation.

  • You are comfortable working across backend and data, and optionally some frontend integration.

  • You communicate clearly, enjoy collaborating with cross-functional partners, and can explain complex AI behavior to non-technical stakeholders.

  • You are comfortable operating in a startup-like environment, prioritizing impact and iterating quickly while maintaining quality.

Bonus Points
  • Experience in healthcare, EMR, or clinical workflows; familiarity with standards such as HL7/FHIR.

  • Experience with .NET Core, MySQL, and cloud deployment (Azure is a plus).

  • Familiarity with security and compliance practices such as HIPAA and SOC2-style controls, including RBAC and audit logging.

  • Experience building evaluation pipelines (golden datasets, offline eval, red-teaming, prompt regression tests).

  • Experience with vector databases/search or building retrieval layers over relational and document stores.

  • Knowledge of PHI de-identification, redaction, and safe data handling.

Tech Stack
  • Frontend: React + Vite + TypeScript

  • Backend: .NET Core services

  • Database: MySQL

  • Cloud: Azure, AWS

  • AI: LLM APIs, retrieval/vector search, observability & evaluation tooling

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