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Vector Databases Jobs in Utah (NOW HIRING)

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 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 ...

United IT

Junior AI Engineer

Salt Lake City, UT · On-site

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 ...

Patientco

Senior ML Engineer

Lehi, UT · On-site

$98.10K - $134.70K/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 ...

Bausch + Lomb

AI Agent ML Engineer

Salt Lake City, UT · On-site

$165K - $190K/yr

Build and maintain data pipelines, embeddings, and vector databases to support agent intelligence. * Optimize models for scalability, latency, and accuracy in production environments. * Champion ...

Waystar

Senior ML Engineer

Lehi, UT · On-site

$98.10K - $134.70K/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

$98.10K - $134.70K/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

$98.10K - $134.70K/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 ...

Studio McGee

Senior Data Engineer

Draper, UT · On-site

$99.10K - $134.60K/yr

Evaluate and implement tools for connecting internal data to LLM-based workflows, including vector databases, chunking strategies, and metadata schemas Knowledge, Skills & Abilities: * Strong ...

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Vector Databases information

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 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 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 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 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 Holdings, LLC

Salt Lake City, UT • On-site

Full-time

Posted 19 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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