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Vector Databases Jobs in Clearfield, UT (NOW HIRING)

InfiCare

Java Engineer

Salt Lake City, UT · On-site

$50 - $68.75/hr

PostgreSQL, MongoDB, Redis 4. AI Ecosystem Awareness of modern AI concepts including LLMs, RAG, AI Agents, and vector databases Ability to integrate AI capabilities into enterprise applications ...

TAB Bank

Software Engineer IV

Ogden, UT

You'll play a key role in modernizing legacy platforms, integrating emerging technologies (including LLMs and vector databases), and enabling the bank's next generation of digital capabilities. Along ...

TAB Bank

Software Engineer IV

Ogden, UT · On-site

You'll play a key role in modernizing legacy platforms, integrating emerging technologies (including LLMs and vector databases), and enabling the bank's next generation of digital capabilities. Along ...

Octanner

Sr. Applied AI Engineer

Salt Lake City, UT

$101K - $138K/yr

Deployed RAG systems including embedding models, vector databases, hybrid search, and retrieval optimization * Designed LLM strategies covering tool calling, structured outputs, prompt engineering ...

New

Sitreps 2 SteerCos

Product Manager - AI Applications

UT · On-site +1

Partner on Platform & Quality Standards Work with Engineering to define AI infrastructure requirements including vector databases, prompt frameworks, and model observability. Set quality benchmarks ...

Pwc

ERP AI Engineer - Manager

Salt Lake City, UT · 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

CTIO AI Engineering Manager

Salt Lake City, UT

$73K - $244K/yr

... vector databases and orchestration tools like LangChain - Translating complex business problems into software-engineered AI solutions - Deploying on cloud platforms like AWS, GCP, Azure ...

Vector CSP LLC

Stan/Eval Support

Hill Air Force Base, UT · On-site

$50K - $57K/yr

... database systems. * Interface with HARM personnel to update the ARMS (or future Government-mandated ... Vector CSP, LLC is an Equal Opportunity Employer. We do not discriminate in employment decisions ...

Vector CSP LLC

Stan/Eval Support

Hill Air Force Base, UT · On-site

$50K - $57K/yr

... database systems. * Interface with HARM personnel to update the ARMS (or future Government-mandated ... Vector CSP, LLC is an Equal Opportunity Employer. We do not discriminate in employment decisions ...

D.A. Davidson

AI & Automation Engineer

Salt Lake City, UT · On-site

$90K - $120K/yr

... with vector databases for domain-specific Q&A. Experience with Azure AI Foundry and Azure AI capabilities like document intelligence, computer vision, speech, and more. • Financial Services ...

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All JobsVector Databases JobsVector Databases Jobs in Clearfield, UT

Vector Databases information

What is the salary of a vector database developer?

The salary of a vector database developer typically ranges from $80,000 to $150,000 annually, depending on experience, location, and company size. Skilled developers with expertise in machine learning, data structures, and database management may earn higher salaries, especially in tech hubs or with advanced certifications.

Are vector databases the future?

Vector database jobs involve managing and optimizing databases designed for high-dimensional vector data, which are essential for AI and machine learning applications. As AI continues to grow, demand for professionals skilled in vector database technologies and related tools like embedding models is expected to increase, making this a promising field for future job opportunities.

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 can you do with a vector database?

A vector database is used in roles involving data management and machine learning to store, search, and retrieve high-dimensional vector representations of data such as images, text, or audio. It enables efficient similarity searches, supporting applications like recommendation systems, natural language processing, and computer vision. Working with a vector database often requires knowledge of data structures, indexing techniques, and programming skills in languages like Python or C++.

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 the top 5 vector databases?

Top vector databases used in data management and AI applications include Pinecone, Weaviate, FAISS, Milvus, and Annoy. These databases are optimized for storing and searching high-dimensional vector data, often requiring skills in machine learning and database management. They are widely adopted for tasks like similarity search and recommendation systems.
What cities near Clearfield, UT are hiring for Vector Databases jobs? Cities near Clearfield, UT with the most Vector Databases job openings:
Vector Databases jobs near you
Java Engineer

Java Engineer

InfiCare

Salt Lake City, UT • On-site

$50 - $68.75/hr

Other

Posted 4 days ago

Job description

Java Engineer

Location: Salt Lake City, Utah onsite

C2C Contract 

JD Below

1. Core Java Expertise (Kotlin is a plus)

Strong in Core Java, multithreading, collections, JVM concepts, and performance tuning

Ability to write clean, scalable, and secure enterprise-grade code

Kotlin knowledge is an added advantage for modern backend development

Tech Stack: Java 17+, Kotlin, Maven/Gradle, JUnit

2. Spring Boot & Microservices

Strong hands-on experience with Spring Boot frameworks and microservices architecture

Knowledge of distributed systems, resiliency patterns, and event-driven architecture

Ability to design scalable and loosely coupled services

Key Skills: REST APIs, Kafka,

Good to have: Circuit Breaker, Saga, Docker, Kubernetes

3. Cloud-Native & Database Engineering

Understanding of cloud-native principles and scalable backend design

Hands-on experience with SQL and NoSQL databases

Strong in database design patterns, performance optimization, caching, and scalability

Tech Stack: PostgreSQL, MongoDB, Redis

4. AI Ecosystem 

Awareness of modern AI concepts including LLMs, RAG, AI Agents, and vector databases

Ability to integrate AI capabilities into enterprise applications securely and responsibly

Good Skills to have:  LangChain, Prompt Engineering, Semantic Search

5. API Strategy & Enterprise Integration

Strong understanding of API-first architecture and enterprise integration patterns

Ability to design secure, reusable, and scalable APIs with proper governance

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