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Vector Databases Jobs in Minnesota (NOW HIRING)

Azure AI Lead

Hamel, MN · On-site

$68 - $88.75/hr

Experience with vector databases such as Azure AI Search. Strong Python experience, including data processing, API development, and Streamlit. Good understanding of LLMs, embeddings, RAG architecture ...

Knowledge of vector databases such as PGVector and ChromaDB. * Front-end development experience using React JS. * Understanding of API design, backend services, and distributed AI systems.

... vector databases, indexing strategies (like chunking, hierarchical indexing), and hybrid search approaches, response quality evaluation evaluation metrics. o Strong proficiency in Natural Language ...

Senior Agentic AI Developer

Minneapolis, MN · On-site

$57 - $75.25/hr

... and vector database solutions. • Build and manage multi-agent workflows and orchestration systems. • Optimize prompts, agent performance, response quality, and operational efficiency. • ...

Data Scientist - Remote

Minnetonka, MN · On-site +1

$112K - $193K/yr

Implementing vector databases * Developing agentic workflows with LangChain * Integrating AI into enterprise applications for Claims Payment Integrity (PI) * The role partners closely with Data ...

Data Scientist - Remote

Minnetonka, MN · On-site

$112K - $193K/yr

Implementing vector databases * Developing agentic workflows with LangChain * Integrating AI into enterprise applications for Claims Payment Integrity (PI) * The role partners closely with Data ...

Python Developer

Minnetonka, MN · On-site

$51.25 - $70.50/hr

Familiarity with vector databases (Pinecone| FAISS| ChromaDB| etc.) and embedding generation. Strong understanding of REST APIs| microservices| and modular design for scalable AI systems. Exposure to ...

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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 cities in Minnesota are hiring for Vector Databases jobs? Cities in Minnesota with the most Vector Databases job openings:
Azure AI Lead

$68 - $88.75/hr

Other

Posted 4 days ago


Job description

Azure AI Foundry Specialist

Strong hands-on experience with Azure AI Foundry.

Strong experience with Azure Document Intelligence.

Experience with vector databases such as Azure AI Search.

Strong Python experience, including data processing, API development, and Streamlit.

Good understanding of LLMs, embeddings, RAG architecture, and prompt engineering.

Strong experience within the Microsoft Azure ecosystem, including Azure OpenAI, Storage, App Service/Functions, and Key Vault.

Strong SQL or Snowflake experience.

Proven experience delivering real-world, production-grade document intelligence projects.

Excellent business communication and stakeholder interaction skills.

5+ years of relevant experience in AI/ML, data, or applied machine learning.

Design and implement end-to-end document intelligence solutions using Azure AI Foundry.

Build and manage LLM workflows, prompts, and evaluations using Azure AI Foundry and Prompt Flow.

Develop RAG pipelines leveraging vector databases such as Azure AI Search.

Perform document ingestion, OCR, preprocessing, chunking, and normalization using Python.

Build and expose REST APIs for AI services using Python.

Apply prompt engineering techniques to improve response accuracy and grounding.

Evaluate and select OCR, embedding, and LLM models based on business needs.

Troubleshoot and optimize AI pipelines for performance and scalability.

Write and optimize SQL or Snowflake queries for data analysis and validation.

Build quick internal tools and demos using Streamlit for business users.

Communicate effectively with stakeholders regarding requirements, results, and limitations.

Experience with LLM evaluation and monitoring.

Exposure to MLOps, CI/CD, or Azure DevOps.

Experience with invoice, contract, or form-processing use cases.

Azure AI Foundry.

Azure Document Intelligence.

Azure AI Search / vector databases.

Python.

REST API development.

Streamlit.

LLMs.

Embeddings.

RAG architecture.

Prompt engineering.

Azure OpenAI.

Azure Storage.

Azure App Service / Azure Functions.

Azure Key Vault.

SQL.

Snowflake.

Relevant degree in Computer Science, Information Technology, Data Science, Artificial Intelligence, or related field preferred.


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About Cynet Systems

Sourced by ZipRecruiter

Cynet Systems Inc is a staffing and recruiting corporation nestled in Ashburn, VA, USA. Established in 2010, the company operates within the Information Technology and Services sector, specializing in providing effective workforce solutions to different business needs, including IT consulting, direct hire, and contract staffing services. Through the years, Cynet Systems has built an impressive portfolio, going beyond borders and expanding its operations internationally in Canada and India. Rooted in its core values of teamwork, leadership, and commitment, Cynet Systems helps businesses unlock their full potential by providing versatile and competent professionals that perfectly align with their needs. Fueled by their unwavering mission to deliver top-tier talent to businesses worldwide, Cynet Systems garnered various recognitions including SIA's fastest-growing staffing firms and Best Place to Work in Virginia for 2019.

Industry

It services

Company size

501 - 1,000 Employees

Headquarters location

Sterling, VA, US

Year founded

2010

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