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Vector Databases Jobs in Ogden, UT (NOW HIRING)

Google AI Lead Architect

Salt Lake City, UT · On-site

$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 ...

Showing results 21-26

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:

Consultant - AI Solutions Architect

Cicero Group

Salt Lake City, UT • On-site, Remote

Full-time

Re-posted 22 days ago


Job description

About the Role
MGT is hiring AI Solutions Architects to join our AI Operating Group (AI OG). The AI OG is responsible for both internal AI tool development and client-facing AI solution delivery, primarily serving the state and local government market.
This is a hands-on role. You will design and build AI-powered solutions for real client problems, not write slide decks about what AI could do. You will work across the full lifecycle: discovery, architecture, build, deployment, and iteration. The right person for this role sits comfortably at the intersection of technical capability and business problem-solving.
This is not a traditional software engineering position. It is the cross roads of consulting and building. We need people who can sit in a room with a client, understand their operational challenge, and translate that into a working AI architecture. You should be comfortable with concepts like RAG pipelines, agentic workflows, prompt engineering, and cloud deployment. But your real value is in how you think and solve problems, not just what you can code.
What You'll Do
• Design end-to-end AI solution architectures for client engagements, from problem discovery through production deployment
• Build and implement RAG pipelines, agentic workflows, and multi-agent systems using frameworks such as LangChain and LangGraph
• Architect data pipelines and integration patterns that connect AI capabilities to existing client systems and data sources
• Work with tool-layer integration standards such as MCP (Model Context Protocol) to connect agents with enterprise systems
• Develop reusable components, skills, templates, and accelerators that scale across multiple engagements
• Collaborate with project leads, consultants, and subject matter experts to translate business requirements into technical specifications and solution designs
• Support internal product development across MGT's AI tool suite
• Present technical architectures and solution recommendations to both internal leadership and client stakeholders
• Participate in discovery sessions and workshops to identify high-impact AI use cases within client organizations
Required Qualifications
• 3-5 years of professional experience; does not have to be in AI, but must demonstrate strong technical problem-solving ability and a track record of building solutions that work
• Hands-on experience with one or more of the following: LLM-based application development, retrieval-augmented generation (RAG) architectures, agentic design patterns, prompt engineering, or vector databases
• Demonstrated ability to learn new technical capabilities and platforms quickly
• Strong communication skills; you will work with consultants, clients, and executive leadership, not just engineers
• Ability to independently scope, architect, and deliver technical solutions with minimal oversight
• A builder mentality: you would rather figure it out and ship it than wait for a detailed specification
• Bachelor's degree or equivalent practical experience
Preferred Qualifications
• Experience in consulting, professional services, or client-facing technical delivery
• Background in data architecture, ETL/ELT pipelines, or analytics platforms
• Programming experience in Python or similar languages
• Experience working with cloud-based AI services on any major platform
What Success Looks Like
• Within 30 days: You understand our current tool suite, architecture patterns, and active client engagements. You have shipped something.
• Within 90 days: You are independently architecting solutions for client projects and contributing to internal product development.
• Within 6 months: You are leading technical delivery on engagements, mentoring team members on AI solution patterns, and shaping our approach to new use cases.
Why MGT
You will be joining a team that is actively building and shipping AI products, not talking about them. We have real tools in production, real clients using them, and a leadership team that understands both the technology and the business.
This is a high-visibility role with direct access to senior leadership and the opportunity to shape how AI gets delivered across the organization. We are building the AI practice from the ground up, which means you will have an outsized impact on the tools, processes, and culture of the team.
Our philosophy: clean data foundations first, AI layered on top. Process understanding before automation. Empowering domain experts to build, not centralizing AI capability in a silo. If that resonates with you, this is your team.
NOTE: We currently do not accept candidates who require sponsorship, nor are we able to provide sponsorship opportunities to candidates.
Positions are available in Salt Lake City, Utah; Washington, D.C.; and remote. Remote employees will be expected to travel to an office periodically.