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Vector Databases Jobs in Ogden, UT (NOW HIRING)

Java Developer with AI

Salt Lake City, UT · On-site

$49.25 - $63.75/hr

... using vector databases such as Pinecone, ChromaDB, Weaviate, Milvus, or FAISS. - Experience integrating AI models such as OpenAI GPT, Azure OpenAI, Anthropic Claude, Gemini, or Llama. - Strong ...

Stay current with advancements in LLMs, vector databases, and agent frameworks * Experiment with new tools and techniques to improve speed, quality, and capability * Contribute reusable patterns ...

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 ...

Sr. Applied AI Engineer

Salt Lake City, UT · On-site

$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 ...

Sr. Applied AI Engineer

Salt Lake City, UT · On-site

$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 ...

... 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 ...

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

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

... 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 ...

... 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 ...

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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 Ogden, UT? For Vector Databases jobs in Ogden, UT, the most frequently searched job titles are:
What cities near Ogden, UT are hiring for Vector Databases jobs? Cities near Ogden, UT with the most Vector Databases job openings:

Data Scientist and AI Specialist

NexOne, Inc.

Clearfield, UT • On-site

Full-time

Posted 4 days ago


Job description

About the Role
We are seeking an innovative and driven Data Scientist / AI Specialist to join our advanced technology team. In this role, you will architect, build, and deploy cutting-edge AI solutions powered by Large Language Models (LLMs), Vector Databases, and Knowledge Graphs. You will play a pivotal role in bridging the gap between raw unstructured data and intelligent, context-aware applications that drive business impact.
Key Responsibilities
  • LLM Integration & Fine-Tuning: Design, implement, and optimize applications utilizing Large Language Models (LLMs) via APIs and open-source frameworks (e.g., LangChain, LlamaIndex).
  • Vector Database Architecture: Architect, manage, and scale vector search infrastructure (e.g., Pinecone, Milvus, Qdrant, Weaviate, pgvector) for high-performance semantic search and retrieval-augmented generation (RAG) pipelines.
  • Knowledge Graph Development: Design and construct Knowledge Graphs to structure complex domain data, integrating them with LLMs and vector search to improve reasoning and context retrieval (GraphRAG).
  • Python Scripting & Pipeline Automation: Write robust, production-ready Python code to automate data ingestion, preprocessing, embedding generation, and model evaluation.
  • Collaboration & Deployment: Work closely with cross-functional teams (Software Engineers, Data Engineers, and Product Managers) to deploy AI models into scalable cloud production environments.
Required Qualifications
  • Experience: 3+ years of professional experience in Data Science, Machine Learning, or Artificial Intelligence, with a strong focus on NLP and generative AI.
  • Programming: Advanced proficiency in Python scripting and modern ML libraries
  • Education: BS in Technical Domain