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Vector Databases Jobs in Greeley, CO (NOW HIRING)

BillGO, Inc.

AI Data Analytics Engineer

Fort Collins, CO · On-site

$102K - $146K/yr

... vector databases, embeddings, and orchestration frameworks such as LangChain, LangGraph, or similar. * Solid engineering fundamentals: data modeling, APIs, cloud platforms, CI/CD, testing, and ...

BillGO, Inc.

AI Data Analytics Engineer

Fort Collins, CO

$113K - $135K/yr

... vector databases, embeddings, and orchestration frameworks such as LangChain, LangGraph, or similar. * Solid engineering fundamentals: data modeling, APIs, cloud platforms, CI/CD, testing, and ...

All JobsVector Databases JobsVector Databases Jobs in Greeley, CO

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

AI Data Analytics Engineer

BillGO, Inc.

Fort Collins, CO • On-site

$102K - $146K/yr

Full-time

Posted 26 days ago

Job description

BillGO is building the next generation of payments. Our vision is to be the payment accelerator for Small Business: an intelligent network that helps businesses get paid faster, operate leaner, and grow with confidence.
We are hiring an AI Data Analytics Engineer to design, build, and ship the data, analytics, and AI capabilities that power BillGO's AI/Data Platform, from trusted data models and self-serve analytics that speed up internal decision-making to AI-native features that turn data into value for Small Businesses. This is a hands-on role that blends analytics engineering with applied AI at the center of BillGO's AI-native strategy.
You will sit within BillGO's Data Platform & Intelligence organization, partnering closely with Application Engineering, Platform Engineering, and Product to move data and AI capabilities from idea to production. Your work turns BillGO's AI Three-Level Framework, Internal Efficiency, Revenue Acceleration, and Customer Value, into shipped, reliable analytics and AI software.
WHAT YOU WILL OWN:
  • The design, build, and delivery of data models, analytics pipelines, and AI/ML features embedded in BillGO's payments products and internal tools.
  • Warehouse and semantic-layer modeling, metrics definitions, and self-serve analytics that make trusted data accessible across the business.
  • Integration of large language models, embeddings, and retrieval-augmented generation (RAG) systems that turn analytics data into intelligent experiences.
  • Data pipelines, evaluation frameworks, and monitoring that keep analytics and AI features accurate, safe, and observable in production.
  • Prompt engineering, model selection, and build-versus-buy tradeoffs balancing quality, latency, and cost.
  • Responsible and secure use of data and AI appropriate for a regulated payments environment.
  • Partnership with Application Engineering, Platform Engineering, and Data Platform & Intelligence to embed analytics and AI into existing services and APIs.

WHAT WE ARE LOOKING FOR:
  • 3-5 years of experience building and shipping production software or data products, with meaningful experience in analytics engineering and AI/ML feature development.
  • Proficiency in Python and SQL, and experience with modern data and AI/ML tooling: data warehouses (e.g., Snowflake, BigQuery, Redshift), transformation frameworks (e.g., dbt), LLM APIs, vector databases, embeddings, and orchestration frameworks such as LangChain, LangGraph, or similar.
  • Solid engineering fundamentals: data modeling, APIs, cloud platforms, CI/CD, testing, and observability.
  • Understanding of BI and analytics tooling, semantic layers, prompt engineering, RAG, model evaluation, and guardrail or safety practices.
  • Experience with data pipelines and both structured and unstructured data.
  • Fintech, payments, or regulated-industry experience is a plus but not required.
  • Strong collaboration and communication skills; comfortable working cross-functionally with product, platform, and data teams.

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