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Vector Databases Jobs in Utah (NOW HIRING)

... 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 ...

Sr. Data Engineer

Draper, UT · Hybrid

$107K - $128K/yr

Design and manage vector databases and embedding pipelines to support semantic search and Retrieval-Augmented Generation (RAG). * Build and optimize retrieval pipelines including hybrid search ...

... 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 ...

You have substantive, hands-on experience building and deploying LLM-based solutions - RAG pipelines, vector databases, agent frameworks, prompt engineering at scale. Using Copilot or ChatGPT at work ...

You have substantive, hands-on experience building and deploying LLM-based solutions -- RAG pipelines, vector databases, agent frameworks, prompt engineering at scale. Using Copilot or ChatGPT at ...

Google AI Lead Architect

Salt Lake City, UT

$53.50 - $73.25/hr

Build RAG and agentic solutions using Vertex AI Vector Search and BigQuery vector; implement ... databases. Should have experience in leveraging various GenAI tools to accelerate software ...

Senior Data Engineer

American Fork, UT

$94K - $128K/yr

Contribute to AI data infrastructure-support RAG pipelines, vector storage, and Snowflake Cortex integrations as one component of the broader engineering scope. * Mentor junior engineers and build ...

Senior Data Engineer

American Fork, UT · On-site

$94K - $128K/yr

Contribute to AI data infrastructure--support RAG pipelines, vector storage, and Snowflake Cortex integrations as one component of the broader engineering scope. * Mentor junior engineers and build ...

Showing results 41-60

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 cities in Utah are hiring for Vector Databases jobs? Cities in Utah with the most Vector Databases job openings:

Data Scientist - Applied AI Scientist

Enterprise Technology Operations

Midvale, UT • Hybrid

Other

Medical, Dental, Vision, Life, Retirement, PTO

Re-posted 8 days ago


Job description

Zions Bancorporation's Enterprise Technology and Operations (ETO) team is transforming what it means to work for a financial institution. With a commitment to technology and innovation, we have been providing our community, clients and colleagues the best experience possible for over 150 years. Help us transform our workforce of the future, today.

Zions Bancorporation's Innovation Lab is seeking a creative and driven Data Scientist (Applied AI Scientist) who bridges the gap between rigorous statistical research and production-grade software engineering. This role is at the heart of our innovation engine. You will not only uncover deep data insights and design advanced AI algorithms, but you will also architect the robust, scalable code required to bring those concepts to life.

As a key member of the Innovation Lab, you will work in a fast-paced, experimental environment, turning ambiguous business challenges into tangible, data-driven prototypes. We need a scientist who treats machine learning as an engineering discipline, someone who understands the "why" behind the math, and the "how" of robust software implementation.

Visa Sponsorship:
This Data Scientist position is currently NOT eligible for employment visa sponsorship (e.g., H-1B visa). This includes, for example, situations where a candidate may have temporary work authorization while enrolled in school or upon graduation (e.g., CPT, OPT) but would need H-1B visa sponsorship within a few years of employment in order to maintain employment eligibility.

Responsibilities:

  • End-to-End AI Design: Design, prototype, and validate ML/AI solutions, translating complex business challenges into mathematical formulations and scalable, production-ready code.
  • Advanced Analytics & EDA: Perform deep exploratory data analysis, statistical testing, and data transformations on diverse datasets (structured and unstructured) to uncover predictive signals and validate hypotheses.
  • Production-Grade Science: Architect and implement modular, extensible, and testable Python codebases for AI experiments. Move beyond Jupyter notebooks by applying clean-code principles (SOLID, DRY) for seamless hand-off to ETO Engineering teams.
  • Agentic & Generative AI: Develop and experiment with applied generative AI and multi-agent architectures using orchestration frameworks (e.g., LangChain, LangGraph), focusing on optimal state management, robust RAG pipelines, and efficient system design.
  • Algorithmic Optimization: Optimize model inference, data processing pipelines, and memory footprints for latency and scalability, applying a strong understanding of data structures and algorithmic complexity.
  • Rigorous Evaluation: Build automated evaluation frameworks to benchmark model performance, mitigate hallucinations, track drift, and ensure algorithmic fairness via A/B testing and statistical rigor.
  • Collaboration & Communication: Act as the technical translator between research-focused ideation and engineering execution. Communicate complex statistical findings and system architectures to both technical and non-technical stakeholders.

Qualifications:

  • The Scientist's Mind: Solid foundation in statistics (Bayesian/Frequentist), linear algebra, hypothesis testing, and the internal mechanics of ML algorithms (e.g., how optimizers work, loss functions, attention mechanisms).
  • The Engineer's Toolbelt: Advanced Python proficiency with a strong focus on Object-Oriented Programming (OOP) and modular design. You must be comfortable writing unit tests (e.g., Pytest) for your data pipelines and models.
  • Framework Depth: Deep expertise with ML libraries (PyTorch, TensorFlow, Scikit-learn, Pandas) and experience implementing custom logic, rather than just calling out-of-the-box models.
  • Generative AI Systems: Hands-on experience with NLP, Large Language Models (LLMs), and Vector Databases, with an understanding of how to evaluate and optimize these systems at scale.
  • Software Maturity: Proficiency with Git/version control, containerization (Docker), API development (FastAPI/Flask), and a working knowledge of how models fit into a CI/CD lifecycle (MLOps).
  • Problem Solving: Exceptional problem-solving skills, comfort with ambiguity, and the ability to own the data science lifecycle from abstract ideation to engineered prototype.
  • Education & Experience: Bachelor's degree in Data Science, Computer Science, Statistics, Mathematics, or a related quantitative field plus 4+ years of hands-on experience in applied machine learning or data science. A Master's degree or PhD is a plus. A combination of education and experience may meet qualifications.

Location:

This position has a hybrid work from home schedule with a minimum of three days per week in the office at the new Zions Technology Center in Midvale, UT.

The Zions Technology Center is a 400,000-square-foot technology campus in Midvale, Utah. Located on the former Sharon Steel Mill superfund site, the sustainably built campus is the company's primary technology and operations center. This modern and environmentally friendly technology center enables Zions to compete for the best technology talent in the state while providing team members with an exceptional work environment with features such as:

  • Electric vehicle charging stations and close proximity to Historic Gardner Village UTA TRAX station.
  • At least 75% of the building is powered by on-site renewable solar energy.
  • Access to outdoor recreation, parks, trails, shareable bikes and locker rooms.
  • Large modern cafe with a healthy and diverse menu.
  • Healthy indoor environment with ample natural light and fresh air.
  • LEED-certified sustainable building that features include the use of low VOC-emitting construction materials.

Benefits:

  • Medical, Dental and Vision Insurance - START DAY ONE!
  • Life and Disability Insurance, Paid Parental Leave and Adoption Assistance
  • Health Savings (HSA), Flexible Spending (FSA) and dependent care accounts
  • Paid Training, Paid Time Off (PTO) and 11 Paid Federal Holidays
  • 401(k) plan with company match, Profit Sharing, competitive compensation in line with work experience
  • Mental health benefits including coaching and therapy sessions
  • Tuition Reimbursement for qualifying employees
  • Employee Ambassador preferred banking products

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