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Vector Databases Jobs in Springdale, AR (NOW HIRING)

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

Principal, Data Scientist

Tontitown, AR · On-site

$110K - $220K/yr

Leverage semantic search, vector databases, embeddings, NLP, LLMs, and Generative AI technologies to build intelligent audit and decision-support systems. * Architect enterprise-grade AI solutions ...

Principal, Data Scientist

Greenland, AR · On-site

$110K - $220K/yr

Leverage semantic search, vector databases, embeddings, NLP, LLMs, and Generative AI technologies to build intelligent audit and decision-support systems. * Architect enterprise-grade AI solutions ...

Principal, Data Scientist

Decatur, 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.

Principal, Data Scientist

Bentonville, 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.

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.

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

Principal, Data Scientist

Elkins, 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.

Principal, Data Scientist

Pea Ridge, 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.

Principal, Data Scientist

Lowell, 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.

Principal, Data Scientist

Johnson, 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.

Principal, Data Scientist

Centerton, AR · On-site

$110K - $220K/yr

Leverage semantic search, vector databases, embeddings, NLP, LLMs, and Generative AI technologies to build intelligent audit and decision-support systems. * Architect enterprise-grade AI solutions ...

Principal, Data Scientist

Noel, MO · 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.

Principal, Data Scientist

Gravette, 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.

Principal, Data Scientist

Rogers, AR · On-site

$110K - $220K/yr

Leverage semantic search, vector databases, embeddings, NLP, LLMs, and Generative AI technologies to build intelligent audit and decision-support systems. * Architect enterprise-grade AI solutions ...

Leverage semantic search, vector databases, embeddings, NLP, LLMs, and Generative AI technologies to build intelligent audit and decision-support systems. * Architect enterprise-grade AI solutions ...

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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Showing results 1-20

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.
Senior IT Data Engineer (Onsite)

Senior IT Data Engineer (Onsite)

Tyson Foods

Springdale, AR

$93K - $127K/yr

Full-time

Medical, Dental, Vision, Life, Retirement, PTO

Posted 9 days ago


Tyson Foods rating

6.4

Company rating: 6.4 out of 10

Based on 523 frontline employees who took The Breakroom Quiz

256th of 388 rated food and drinks producers


Job description

Job Details:

The Senior IT Data Engineer is an expert in building and optimizing modern data platforms, including real-time streaming pipelines that are cost-optimized for cloud resources. This role leads complex projects across data engineering, enterprise data modeling, and agentic AI - architecting scalable solutions, enforcing governance and security, and deploying AI-powered autonomous workflows that transform data engineering practices.

This position requires working side by side with users of the solution, understanding the opportunities, and rapidly iterating on the solution; architecting and building solutions that leverage business-critical data and the latest advancements in AI to solve them. You'll work in small, agile teams and own the end-to-end execution and implementation of high-stakes projects for Tyson's extensive manufacturing footprint. Very few companies provide the opportunity to work end-to-end projects and initiatives with such massive scale with significant could and data infrastructure already in place.

With over 100+ manufacturing facilities worldwide, this position will be front and center influencing change and deploying technology to more than 100,000 team members.

Essential Duties and Responsibilities
  • Lead the design and orchestration of complex data pipelines and ETL/ELT processes using Python, SQL, and modern frameworks (e.g., dbt, Airflow, Dagster) for a $50B company.
  • Architect scalable data solutions using modern platforms ( BigQuery), lakehouse patterns (Delta Lake, Iceberg), and event-driven streaming architectures (Kafka, Flink, Pub/Sub).
  • Must be able to design enterprise-wide data models using advanced techniques - dimensional modeling, multi-dimensional modeling, ERDs - ensuring consistency and alignment with business processes.
  • Define and implement data contracts and APIs to ensure reliable interfaces between data producers and consumers.
  • Establish and enforce data governance, security, cataloging, and stewardship standards across all data and AI systems.
  • Optimize cloud costs (AWS, GCP, or Azure) through efficient architecture and resource management.
  • Implement CI/CD pipelines for data workflows and manage containerized workloads (Docker, Kubernetes) with infrastructure as code (Terraform).
  • Must be able to work with DBT to model relevant data sources and ensure quality and uptime of that data
  • Drive data observability, including proactive monitoring, alerting, and automated detection of freshness, volume, and schema drift issues.
  • 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.
  • Implement AI guardrails, observability, and evaluation frameworks, including LLMOps practices (prompt versioning, A/B testing, drift monitoring), cost optimization (token strategies, model selection), and security measures (prompt injection prevention, PII handling).
  • Lead code reviews, establish coding standards, perform other assigned job-related duties that align with our organization's vision, mission, and values and fall within your scope of practice.
  • Collaborating with fellow engineers on architecture and design decisions.
  • Must be able to work with the other developers on the team, specifically the data scientist and AI engineers to assist with what they need.
  • Wrangling massive-scale data and using AI to accelerate and enhance critical operations.
  • Developing custom applications tailored to customer needs.
  • Engaging directly with customer stakeholders, from consumers to technical teams and executives.
Qualifications
  • Education: Bachelor's Degree or relevant experience.
  • Preferred Certification(s): AWS Solutions Architect Professional, Google Professional Data Engineer, Databricks Certified Data Engineer Professional, or equivalent.
  • Experience: 3+ years of relevant and practical experience.
Special Skills
  • Proficiency in Python and SQL for data engineering at scale.
  • Expertise in modern data platforms (Databricks, Snowflake, BigQuery), lakehouse architectures (Delta Lake, Iceberg), and streaming (Kafka, Flink, Pub/Sub).
  • Deep knowledge of GCP.
  • Hands-on experience with orchestration (Airflow, Dagster), transformation (dbt), containerization (Docker, K8s), and IaC (Terraform).
  • Advanced data modeling, warehousing, dimensional modeling, and data contracts.
  • Expertise in CI/CD, data observability, governance, and cataloging.
  • Advanced expertise in agentic AI architectures, multi-agent systems, LLMOps, RAG pipelines, and AI safety/guardrails.
  • A highly analytical approach and eagerness to solve technical problems with data structures, storage systems, cloud infrastructure, front-end frameworks, and other technical tools.
  • Experience or curiosity about working with and using large-scale data to take on valuable business problems.
  • Ability to collaborate efficiently in teams of technical and non-technical individuals, and comfortable working in a dynamic environment with evolving objectives and iteration with users.
Soft Skills
  • Project Management: Leading complex data and AI initiatives end-to-end.
  • Mentorship: Guiding team members in technical and professional growth.
  • Strategic Thinking: Aligning data and AI solutions with organizational goals.
  • Communication: Articulating strategies to technical and non-technical stakeholders.
  • Problem-Solving: Resolving complex data and AI system challenges.
  • Adaptability: Staying current with rapidly evolving technologies and practices.
  • Creativity: Innovating approaches to pipeline design, modeling, and AI automation.
  • Customer obsession
  • Team work & Collaboration

** Not eligible for visa sponsorship now or int he future **

** Not eligible for relocation assistance **

Relocation Assistance Eligible:

No

Work Shift:

1ST SHIFT (United States of America)

Certain roles at Tyson require background checks. If you are offered a position that requires a background check you will be provided additional documentation to complete once an offer has been extended.

Hourly Applicants ONLY -You must complete the task after submitting your application to provide additional information to be considered for employment.

Tyson is an Equal Opportunity Employer. All qualified applicants will be considered without regard to race, national origin, color, religion, age, genetics, sex, sexual orientation, gender identity, disability or veteran status.

We provide our team members and their families with paid time off; 401(k) plans; affordable health, life, dental, vision and prescription drug benefits; and more.

If you would like to learn more about your data privacy rights and how you may use that information, please read our Job Applicant Privacy Notice here.

Unsolicited Assistance: Tyson Foods and its subsidiaries do not accept unsolicited support from external recruitment vendors for open positions within the United States. Any resumes or candidate profiles submitted by recruitment vendors or headhunters to any employee or applicant tracking system at Tyson Foods or its subsidiaries, without a valid written request and search agreement approved by HR, will be considered the property of Tyson Foods. No fees will be paid if the candidate is hired due to an unsolicited referral.


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