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Vector Databases Jobs in Utah (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

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

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 ...

Inficare Technologies

Java Engineer

Salt Lake City, UT · On-site

$50 - $68.75/hr

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 ...

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 ...

Patientco

Senior ML Engineer

Lehi, UT · On-site

$98K - $134K/yr

Generate and manage high-quality vector embeddings for efficient retrieval-augmented generation (RAG) within a Vector Database. * Language Model (LM) Development & Fine-tuning: * Research, select ...

Crew Career Center

Senior ML Engineer

Lehi, UT · On-site

$98K - $134K/yr

Generate and manage high-quality vector embeddings for efficient retrieval-augmented generation (RAG) within a Vector Database. * Language Model (LM) Development & Fine-tuning: * Research, select ...

Waystar

Senior ML Engineer

Lehi, UT · On-site

$98K - $134K/yr

Generate and manage high-quality vector embeddings for efficient retrieval-augmented generation (RAG) within a Vector Database. * Language Model (LM) Development & Fine-tuning: * Research, select ...

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 ...

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All JobsVector Databases JobsVector Databases Jobs in Utah

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 are popular job titles related to Vector Databases jobs in Utah? For Vector Databases jobs in Utah, the most frequently searched job titles are:
What job categories do people searching Vector Databases jobs in Utah look for? The top searched job categories for Vector Databases jobs in Utah are:
What cities in Utah are hiring for Vector Databases jobs? Cities in Utah with the most Vector Databases job openings:
Vector Databases jobs near you
AI Engineer

AI Engineer

System One

Salt Lake City, UT • On-site

Full-time

Re-posted 7 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

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