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Pinecone Vector Databases Jobs in Minnesota (NOW HIRING)

Blank Metal Inc

AI Engineer

Minneapolis, MN · On-site

$108K - $146K/yr

Familiarity with vector databases (Pinecone, Weaviate, Chroma, etc.) * Experience contributing to frontend features using modern frameworks when needed * Background with API design and system ...

Digital Links Inc

AI/ML Engineer

Minneapolis, MN · On-site

... 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 ...

THOMSON REUTERS

Senior Software Engineer

Eagan, MN · On-site

$124K - $164K/yr

Advanced knowledge of vector databases (Pinecone, Weaviate, Chroma, pgvector), embeddings, semantic search, and information retrieval systems * Experience with prompt engineering and management tools ...

Abotts Inc

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 ...

Apex Systems

AI Engineer

Maple Grove, MN · On-site

Experience with or exposure to RAG pipelines, including embeddings and vector databases (FAISS, Pinecone, OpenSearch). * Comfortable with Python alongside .NET for AI/ML-related work. Everforth Apex ...

Deloitte

Cyber - SecOps - Consultant

Minneapolis, MN · On-site

Qualifications Required: * 2+ years of analytics consulting or industry experience * 2+ years of experience with artificial intelligence development tools, including vector databases such as Pinecone ...

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Pinecone Vector Databases information

What is a Pinecone vector database?

A Pinecone Vector Database is a cloud-based service designed to efficiently store, index, and search high-dimensional vector data, such as embeddings generated by machine learning models. It enables fast similarity search, making it ideal for use cases like semantic search, recommendation systems, and AI-powered applications. Pinecone handles the complexity of scaling and managing vector data, so developers can focus on building intelligent applications without worrying about infrastructure.

What are the key skills and qualifications needed to thrive as a Pinecone vector database engineer, and why are they important?

To thrive as a Pinecone Vector Database Engineer, you need a strong background in computer science, data engineering, and experience with large-scale distributed systems, often supported by a relevant degree or equivalent experience. Proficiency in Python, REST APIs, cloud platforms (AWS, GCP), and vector search technologies, along with familiarity with Pinecone’s SDK and database management, are commonly required. Strong analytical thinking, problem-solving abilities, and effective communication skills help you collaborate with cross-functional teams and deliver scalable solutions. These skills ensure robust database performance, efficient data retrieval, and successful integration of vector search capabilities into real-world applications.

What are some common challenges faced by engineers working with Pinecone vector databases, and how can they be addressed?

Engineers working with Pinecone Vector Databases often encounter challenges such as optimizing vector search performance at scale, ensuring data consistency across distributed systems, and integrating the database with various machine learning pipelines. Addressing these challenges typically involves tuning indexing parameters, monitoring resource utilization, and collaborating closely with data scientists to understand retrieval requirements. Regularly reviewing documentation and participating in community forums can also help engineers stay current with best practices and new features.

What is the difference between Pinecone Vector Databases vs Data Engineers?

AspectPinecone Vector DatabasesData Engineers
Primary RoleManaging and deploying vector database solutions for AI/ML applicationsDesigning, building, and maintaining data pipelines and infrastructure
Skills & CertificationsKnowledge of vector databases, cloud platforms, programming (Python, SQL)Data modeling, ETL processes, cloud services, programming (Python, Java)
Work EnvironmentTech companies, AI startups, cloud providersData-driven organizations, tech firms, finance, healthcare

While Pinecone Vector Databases specialists focus on deploying and managing vector database solutions for AI applications, Data Engineers build and maintain the data infrastructure that supports these systems. Both roles require programming skills and familiarity with cloud platforms, but their core responsibilities differ: one centers on database management, the other on data pipeline development.

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Pinecone Vector Databases jobs near you

AI/ML Tech Lead / Architect - Gen AI & Agentic AI)

Nanda Technologies

Minneapolis, MN • On-site

Contractor

Re-posted 3 days ago

Job description

Email: paul@nandatechnologies.com

Job ID: JPC 6845-1-11/17

Job Title: AI/ML Tech Lead / Architect (Gen AI & Agentic AI)
Location: Minneapolis, MN (Onsite – Local Candidates Only)

Required exp: 12+ years

Visa: USC, GC, H4EAD
Client: Optum
Duration: Long-Term Contract

Key Responsibilities

  • Lead the architecture, design, and development of AI/ML, Gen AI, and Agentic AI solutions.
  • Provide hands-on technical leadership to engineering and data science teams.
  • Architect scalable and secure AI systems, integrating LLMs, vector databases, and agent frameworks.
  • Collaborate with product, data, and engineering teams to define technical roadmaps and solution strategies.
  • Oversee deployment, optimization, and performance tuning of AI solutions in production environments.
  • Ensure adherence to enterprise standards, security guidelines, and best practices.

Required Skills

  • 10+ years of experience in AI/ML engineering, solution architecture, or related roles.
  • Deep expertise in Generative AI, LLMs, prompt engineering, and Agentic AI systems.
  • Strong hands-on experience with Python, ML frameworks (TensorFlow, PyTorch), and LLM ecosystems.
  • Experience with vector databases (FAISS, Pinecone, Weaviate, etc.) and retrieval-augmented generation (RAG).
  • Solid understanding of cloud platforms (AWS/Azure/GCP) and MLOps pipelines.
  • Proven background in architecting enterprise-grade AI solutions.
  • Experience with multi-agent frameworks, orchestration tools, or autonomous agent systems.
  • Background in healthcare or payer/provider environments.
  • Certifications in AI/ML, cloud, or architecture disciplines.