ZipRecruiter

Log In

1

Vector Databases Jobs in Clearfield, UT (NOW HIRING)

System One

AI Engineer

Salt Lake City, UT · On-site

... vector databases, and conversational AI systems • Develop RESTful APIs and microservices (e.g., FastAPI) for model serving • Containerize and orchestrate applications using Docker and Kubernetes ...

System One

AI Engineer

Salt Lake City, UT · On-site

... vector databases, and conversational AI systems • Develop RESTful APIs and microservices (e.g., FastAPI) for model serving • Containerize and orchestrate applications using Docker and Kubernetes ...

System One Holdings, LLC

AI Engineer

Salt Lake City, UT · On-site

... vector databases, and conversational AI systems • Develop RESTful APIs and microservices (e.g., FastAPI) for model serving • Containerize and orchestrate applications using Docker and Kubernetes ...

System One

AI Engineer

Salt Lake City, UT · On-site

... vector databases, and conversational AI systems • Develop RESTful APIs and microservices (e.g., FastAPI) for model serving • Containerize and orchestrate applications using Docker and Kubernetes ...

System One Holdings, LLC

AI Engineer

Salt Lake City, UT · On-site

... vector databases, and conversational AI systems • Develop RESTful APIs and microservices (e.g., FastAPI) for model serving • Containerize and orchestrate applications using Docker and Kubernetes ...

System One

AI Engineer

Salt Lake City, UT · On-site

... vector databases, and conversational AI systems • Develop RESTful APIs and microservices (e.g., FastAPI) for model serving • Containerize and orchestrate applications using Docker and Kubernetes ...

TIA Infotek Inc

Java Engineer

Salt Lake City, UT · On-site

$50 - $68.75/hr

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

InfiCare Technologies

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 ...

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 - Senior Associate

Salt Lake City, UT · 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 ...

Murphy Door

Artificial Intelligence Architect

Ogden, UT · On-site

$61.75 - $81.25/hr

You should have hands on experience with: • Large language models and prompt engineering at an advanced level • Agent based systems and task orchestration • Embeddings, vector databases, and ...

Pwc

ERP AI Engineer - Manager

Salt Lake City, UT

$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 ...

Cicero Group

Senior AI Developer

Salt Lake City, UT · On-site +1

$52.75 - $69.75/hr

Hands-on experience building with LLMs, including RAG architectures, agentic design patterns, prompt engineering, or vector databases * Demonstrated Azure experience, including Azure OpenAI Service ...

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 ...

next page

Showing results 1-20

All JobsVector Databases JobsVector Databases Jobs in Clearfield, UT

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 Clearfield, UT? For Vector Databases jobs in Clearfield, UT, the most frequently searched job titles are:
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
Infographic showing various Vector Databases job openings in Clearfield, UT as of June 2026, with employment types broken down into 62% Full Time, 29% Part Time, and 9% Contract. Highlights an 68% Physical, 3% Hybrid, and 29% Remote job distribution.
AI Engineer

AI Engineer

System One

Salt Lake City, UT • On-site

Full-time

Posted 17 days ago

Job description

Junior AI Engineer Job Type: Permanent Full Time Location: Salt Lake City, Utah, United States How you'll make an impact • Design and develop AI-driven product features using ML, GenAI, and LLMs • Build and deploy scalable AI systems using cloud-native architectures • Implement RAG pipelines, vector databases, and conversational AI systems • Develop RESTful APIs and microservices (e.g., FastAPI) for model serving • Containerize and orchestrate applications using Docker and Kubernetes • Ensure system reliability, scalability, security, and cost efficiency • Collaborate cross-functionally with product, engineering, and business teams Required qualifications to be successful in this role What you'll bring • Up to 2 years of experience in engineering or related roles • Familiarity with AI agents and agentic frameworks (e.g., LangChain, LangGraph) • Understanding of agent design patterns and evaluation techniques • Experience with Model Context Protocol (MCP) servers • Proficiency in Python and SQL • Hands-on experience with: o AI/ML and Generative AI o Large Language Models (LLMs) and prompt engineering o RAG architectures and vector databases o MLOps practices • Experience with Docker, Kubernetes, and CI/CD pipelines • Understanding of microservices architecture and API development • Knowledge of serverless design, 12-factor apps, autoscaling, and high availability • Strong problem-solving and communication skills

Ref: #404-IT Pittsburgh

Apply