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Pinecone Vector Databases Jobs in Michigan (NOW HIRING)

Reliable Software Resources

AI AWS Data Engineer

Detroit, MI · On-site

$104K - $125K/yr

Experience with vector databases (OpenSearch Vector Engine, Pinecone, Weaviate, pgvector, FAISS, or similar). * Understanding of embeddings, semantic search, RAG, and document indexing concepts.

HARMAN International

Principal Engineer, AI

Novi, MI · On-site

SQL and NoSQL databases (PostgreSQL, DynamoDB), Elasticsearch for search and analytics, and vector databases (Pinecone, Weaviate, FAISS, Milvus, pgvector). • Cloud & Infrastructure: AWS (S3, EC2 ...

Orbotech

AI Software Engineer

Ann Arbor, MI · On-site

$111K - $188K/yr

Knowledge of retrieval-augmented generation (RAG) and vector databases (e.g., Pinecone, FAISS, Azure Cognitive Search) * Vector database (Milvus) for semantic search, along with a knowledge graph ...

KLA Corporation

AI Software Engineer

Ann Arbor, MI · On-site

$111K - $188K/yr

Knowledge of retrieval-augmented generation (RAG) and vector databases (e.g., Pinecone, FAISS, Azure Cognitive Search) * Vector database (Milvus) for semantic search, along with a knowledge graph ...

KLA Corporation

AI Software Engineer

Ann Arbor, MI · On-site

$111K - $188K/yr

Knowledge of retrieval-augmented generation (RAG) and vector databases (e.g., Pinecone, FAISS, Azure Cognitive Search) * Vector database (Milvus) for semantic search, along with a knowledge graph ...

Kla

AI Software Engineer

Ann Arbor, MI · On-site

$111K - $188K/yr

Knowledge of retrieval-augmented generation (RAG) and vector databases (e.g., Pinecone, FAISS, Azure Cognitive Search) * Vector database (Milvus) for semantic search, along with a knowledge graph ...

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Technical Lead (Applied AI)

Detroit, MI · On-site

Agentic SQL retrieval, MCP integration, agentic tool use, as well as vector databases & RAG techniques implementing retrieval-augmented generation patterns using vector stores (e.g., Pinecone ...

Ford Motor Company

AI Software Engineer

Dearborn, MI · On-site

Experience with vector search, hybrid retrieval architectures, or vector databases (Chroma, Qdrant, Pinecone, pgvector). * Experience working with GCP services (Vertex AI, Cloud Run, and BigQuery) or ...

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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 AWS Data Engineer

AI AWS Data Engineer

Reliable Software Resources

Detroit, MI • On-site

$104K - $125K/yr

Other

This job post has expired 1 day ago. Applications are no longer accepted.

Job description

Job Title: AI AWS Data Engineer – Unstructured Data & Document Processing

Location: Detroit, MI

Position Summary

We are looking for a hands-on AWS Data Engineer to build scalable data ingestion and processing pipelines for large volumes of unstructured documents including PDFs, TIFF images, scanned documents, and other image formats. The primary responsibility is to extract structured information from documents using OCR and AI/ML services, transform the data, and store it in relational databases, JSON document stores, and vector databases to support downstream applications, document search, and AI-powered retrieval.

Responsibilities
  • Design and develop scalable data ingestion pipelines on AWS.
  • Process large volumes of PDFs, TIFF files, images, and other unstructured documents.
  • Build OCR and document extraction workflows using AWS AI services and open-source libraries.
  • Extract metadata, entities, tables, and key-value information from documents.
  • Store extracted data into relational databases, JSON document stores, and vector databases.
  • Develop data models that support fast search and retrieval.
  • Implement document chunking, embedding generation, and indexing for semantic search.
  • Optimize pipelines for performance, scalability, reliability, and cost.
  • Build APIs or integration pipelines for downstream web applications.
  • Ensure data quality, monitoring, logging, and error handling throughout the ingestion process.
  • Work closely with AI engineers and application developers to enable enterprise search and retrieval capabilities.
Required Skills
  • 8+ years of Data Engineering experience.
  • Strong Python development skills.
  • Hands-on experience with AWS services such as S3, Lambda, Step Functions, ECS/EKS, Glue, SQS/SNS, and IAM.
  • Experience processing unstructured documents at scale.
  • Strong knowledge of OCR technologies (AWS Textract preferred; experience with Tesseract or similar is a plus).
  • Experience designing ETL/ELT pipelines.
  • Strong SQL and database design skills.
  • Experience storing and querying JSON data.
  • Experience with vector databases (OpenSearch Vector Engine, Pinecone, Weaviate, pgvector, FAISS, or similar).
  • Understanding of embeddings, semantic search, RAG, and document indexing concepts.
  • Experience building REST APIs is a plus.
  • Familiarity with Docker, Git, and CI/CD pipelines.
  • Strong debugging, communication, and problem-solving skills.
Preferred Qualifications
  • Experience with Amazon Bedrock or other LLM platforms.
  • Experience with LangChain or similar AI orchestration frameworks.
  • Knowledge of Apache Spark or distributed data processing.
  • Experience with document management or enterprise search platforms.
Nice to Have
  • Experience building enterprise document search solutions.
  • Exposure to AI/LLM-based information extraction.
  • Knowledge of Elasticsearch/OpenSearch and search optimization.

Experience working with healthcare, legal, financial, or insurance documents

 

Educational Qualifications:

·         Required - Bachelor’s degree in Computer Science, Information Technology, Computer Engineering or closely related or equivalent.

·        

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