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Vector Ai Jobs in Minnesota (NOW HIRING)

AI Engineer (Hybrid)

Saint Paul, MN · Hybrid

$115K - $139K/yr

Strong understanding of modern AI stacks including APIs, foundation models, vector databases, orchestration tools, and retrieval-augmented generation (RAG) patterns. * Ability to build ...

AI Engineer (Hybrid)

Saint Paul, MN · On-site

$115K - $139K/yr

Strong understanding of modern AI stacks including APIs, foundation models, vector databases, orchestration tools, and retrieval-augmented generation (RAG) patterns. * Ability to build ...

Senior AI Engineer

Eden Prairie, MN · On-site

$120 - $160/hr

Solid grounding in RAG architectures, vector databases (Azure AI Search, Pinecone, Vertex Vector Search), embeddings, and semantic retrieval. * Experience with LLM evaluation frameworks ...

... vector databases (Pinecone, Weaviate, etc.) Ability to integrate domain-specific data into AI systems 4. Agentic AI / AI Agents Experience building AI agents / multi-agent systems Familiarity with ...

We are hiring an AI Engineer to build and operate the data, features, and GenAI foundations that ... Implement LLM application patterns including RAG, document ingestion/chunking, embeddings, vector ...

Senior AI/ML Engineer

Eden Prairie, MN · On-site

$106K - $146K/yr

Design and implement retrieval-augmented generation (RAG) pipelines including document ingestion, chunking strategies, embedding generation, and vector database integration * Build agentic AI systems ...

Senior AI/ML Engineer

Eden Prairie, MN · On-site +1

$106K - $146K/yr

Design and implement retrievalaugmented generation (RAG) pipelines including document ingestion, chunking strategies, embedding generation, and vector database integration * Build agentic AI systems ...

Prin AI & Analytics Data Architect

Shakopee, MN · On-site +1

$68.25 - $87.75/hr

Feature stores and vector data stores * Ensure the architecture scales for global, multibusiness use cases * Data Governance, Trust & Risk (By Design) * Architect governancefirst data and AI patterns ...

Showing results 21-40

Vector Ai information

What are the key skills and qualifications needed to thrive as a Vector AI engineer?

To thrive as a Vector AI Engineer, you need strong foundations in mathematics, machine learning, and computer science, often supported by a degree in a related field. Expertise with vector databases (such as Pinecone or FAISS), programming languages like Python, and knowledge of frameworks like TensorFlow or PyTorch are typically required. Excellent problem-solving, analytical thinking, and effective communication skills help you translate complex business requirements into scalable AI solutions. These qualifications are crucial for developing, deploying, and maintaining efficient AI systems that leverage vector search and representation for real-world applications.

What are some common challenges faced by professionals working in Vector AI roles, and how can they be addressed?

Professionals in Vector AI roles often face challenges such as managing large-scale, high-dimensional data, ensuring model scalability, and optimizing search algorithms for speed and accuracy. Collaborating closely with data engineers, software developers, and product managers is crucial to integrate AI vector solutions effectively into products. Staying updated on the latest advancements in vector databases and similarity search techniques can also be demanding, so continuous learning and participation in relevant communities are highly beneficial. Adopting best practices for model evaluation and experiment tracking can help address these challenges and drive project success.

What is the difference between Vector Ai vs Data Analyst?

AspectVector AiData Analyst
Required CredentialsTechnical certifications, programming skillsDegree in statistics, data science, or related field
Work EnvironmentTech companies, AI development teamsBusiness, finance, healthcare sectors
Industry UsageAI, machine learning, software developmentData interpretation, reporting, decision support

Vector Ai professionals focus on developing and implementing AI algorithms, requiring technical skills and programming knowledge. Data Analysts interpret data to inform business decisions, often working with statistical tools. While both roles handle data, Vector Ai is more specialized in AI technology, whereas Data Analysts focus on data insights and reporting.

What is a Vector AI?

Vector AI typically refers to professionals or technologies focused on vector-based artificial intelligence, which involves the use of high-dimensional vectors to represent data and perform machine learning tasks. These experts work on algorithms that process and analyze vector data for applications like image recognition, natural language processing, and recommendation systems. Their work is crucial in making AI systems more efficient at understanding complex patterns in large datasets. In some contexts, 'Vector AI' may also refer to companies or platforms developing such technologies.
What are popular job titles related to Vector Ai jobs in Minnesota? For Vector Ai jobs in Minnesota, the most frequently searched job titles are:
What job categories do people searching Vector Ai jobs in Minnesota look for? The top searched job categories for Vector Ai jobs in Minnesota are:
Infographic showing various Vector Ai job openings in Minnesota as of August 2026, with employment types broken down into 81% Full Time, 16% Part Time, and 3% Contract. Highlights an 67% Physical, 4% Hybrid, and 29% Remote job distribution.

Senior Consultant, AI/ML Ops Engineer

Horizontal Talent

Minneapolis, MN • On-site, Remote

$109K - $149K/yr

Full-time

Posted 3 days ago

New


Job description

Join a senior-level AI and machine learning engineering role focused on building production-ready models, GenAI applications, and the shared platform that supports them. This opportunity is ideal for an experienced engineer who enjoys moving between applied data science, AI product development, and platform engineering to help bring impactful solutions from prototype to production.

Responsibilities
  • Develop and evaluate machine learning models for use cases such as ranking, scoring, forecasting, classification, and survival or time-to-event analysis.
  • Apply strong experimental methods to validate models, assess performance, and review subgroup behavior, calibration, and potential failure modes.
  • Combine model outputs with business logic and domain rules to support clear, trustworthy recommendations.
  • Design and deliver GenAI-enabled applications, including RAG workflows, agents, structured extraction, summarization, and reasoning trails.
  • Build and improve evaluation frameworks for model and prompt changes, including offline and online testing, regression checks, and quality safeguards.
  • Extend and support shared AI platform capabilities such as model access, routing, budget controls, failover, and observability.
  • Own retrieval and grounding components, including embeddings, vector storage, chunking strategies, and retrieval quality tuning.
  • Productionize AI services using modern Python development practices, containers, AWS services, and CI/CD workflows.
  • Monitor deployed solutions for performance, latency, reliability, cost, drift, and overall quality.
  • Partner with data scientists, MLOps professionals, domain experts, and product stakeholders to move solutions into production.
  • Provide technical leadership through design reviews, code reviews, mentoring, and the creation of reusable engineering patterns.
Skills
  • 5+ years of experience building and shipping ML or AI solutions in production environments.
  • Strong foundation in machine learning concepts, feature engineering, model evaluation, and experimental design.
  • Experience with ranking, scoring, or survival/time-to-event modeling.
  • Hands-on experience with GenAI and LLM application development, including RAG, prompt engineering, function or tool calling, embeddings, and vector search.
  • Advanced Python development skills with an emphasis on clean, maintainable, and testable code.
  • Experience building APIs, services, or shared libraries for production use.
  • Experience working with AWS services such as Bedrock, SageMaker, Lambda, and S3.
  • Knowledge of Docker and Git-based development workflows.
  • Understanding of software engineering best practices, including testing, code review, version control, and CI/CD.
  • Ability to think through cost, latency, and reliability considerations for AI systems.
  • Strong communication and collaboration skills with the ability to work across technical and non-technical partners.
Preferred Skills
  • Experience building AI platform components such as gateways, routing layers, multi-tenant tooling, or internal SDKs.
  • Familiarity with agent frameworks, real-time or voice AI, or streaming inference.
  • Experience with vector databases such as Qdrant, OpenSearch, or pgvector.
  • Exposure to infrastructure as code, observability tools, and cloud monitoring practices.
  • Experience supporting AI workloads with operational cost management and optimization practices.
  • Background in healthcare, clinical, or other regulated environments with attention to data governance and auditability.
  • Experience serving models as endpoints and supporting train/serve parity.
  • Comfort working with sensitive data in a governed environment.

Horizontal is committed to fostering an inclusive, respectful, and equitable workplace where different perspectives and experiences are valued. We encourage candidates from all backgrounds to apply and bring their unique strengths to the team.

By applying for this position, you acknowledge and agree that Horizontal Talent may contact you regarding your application using automated technology, including phone calls, SMS/text messages, or email, which may be delivered by our virtual AI recruiter, Alex.