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Vector Databases Jobs in Detroit, MI (NOW HIRING)

Bosch Group

Senior AI Software Engineer

Plymouth, MI

$116K - $153K/yr

Vector Search & Retrieval Integrate, structure, and optimize vector databases (e.g., PGVector, Qdrant, Milvus, or Azure AI Search) to power Retrieval-Augmented Generation (RAG) pipelines and high ...

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Bosch Group

Senior AI Software Engineer

Plymouth, MI · On-site

$116K - $153K/yr

Vector Search & Retrieval Integrate, structure, and optimize vector databases (e.g., PGVector, Qdrant, Milvus, or Azure AI Search) to power Retrieval-Augmented Generation (RAG) pipelines and high ...

New

IntraEdge

AI Data Engineer

Detroit, MI

$113K - $136K/yr

Develop and manage data architectures, including data lakes, data warehouses, and vector databases, to support various AI workloads. * Ensure data quality and governance: Implement data validation ...

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 ...

Stefanini

MLOps Engineer

Dearborn, MI · On-site

Familiarity with vector databases, embeddings, Retrieval-Augmented Generation (RAG), and semantic search architectures.Strong programming experience in Python, including backend development, API ...

Saanvi Technologies

Machine Learning Engineer 3

Dearborn, MI · On-site

$105K - $126K/yr

Familiarity with vector databases, embeddings, Retrieval-Augmented Generation (RAG), and semantic search architectures. Experience working with enterprise-scale data environments, data lakes, and ...

ETAS

Senior AI Software Engineer

Plymouth, MI · On-site

$116K - $153K/yr

... vector databases (e.g., PGVector, Qdrant, Milvus, or Azure AI Search) to power Retrieval-Augmented Generation (RAG) pipelines and high-speed semantic search. • Programmatically interface with ...

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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.
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Vector Databases jobs near you
Gen AI Engineer

Gen AI Engineer

Miracle Software Systems, Inc.

Detroit, MI • On-site

Other

Posted 10 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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