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Pinecone Vector Databases Jobs in Michigan (NOW HIRING)

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 ...

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Technical Lead (Applied AI)

Detroit, MI

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 ...

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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
Gen AI Engineer

Gen AI Engineer

Miracle Software Systems, Inc.

Detroit, MI • On-site

Other

Posted 8 days ago

Job description

Miracle Software Systems is looking for Generative AI Engineer position for Dearborn, MI

Requirement Details:
Position: Generative AI Engineer
Location: Dearborn, MI
Duration: Full time

Description: We are seeking a Generative AI Engineer to design, develop, and deploy AI-driven applications using Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) architectures. In this role, you will build scalable AI solutions that integrate enterprise data with modern cloud platforms, APIs, and vector databases to enable intelligent automation and advanced analytics.ResponsibilitiesDesign and develop Generative AI applications using LLMs.Build and optimize RAG pipelines using vector databases and embeddings.Develop scalable APIs for AI services using frameworks such as FastAPI.Implement prompt engineering and model optimization techniques.Build and manage data pipelines for AI training and retrieval systems.Deploy and maintain AI solutions on Google Cloud Platform (Google Cloud Platform).Apply containerization (Docker) and CI/CD practices for production deployment.Collaborate with cross-functional teams to integrate AI capabilities into enterprise applications.Basic Qualifications5+ years of software engineering experience.Strong proficiency in Python.Experience working with LLMs and Generative AI applications.Experience building RAG systems and working with vector databases.Experience developing REST APIs (FastAPI or similar frameworks).Strong knowledge of SQL and data processing.Experience with cloud platforms such as Google Cloud Platform.Experience with Git and Docker.Preferred QualificationsExperience deploying AI/ML solutions in production environments.Experience integrating LLM APIs (OpenAI or similar platforms).Familiarity with LangChain, LlamaIndex, or similar frameworks.Experience with vector databases (Pinecone, Weaviate, FAISS, etc.).Understanding of MLOps and CI/CD pipelines.

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