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Vector Databases Jobs in Detroit, MI (NOW HIRING)

TEKsystems c/o Allegis Group

AI/ML Engineer

Dearborn, MI · On-site

$50 - $75/hr

RAG & Knowledge Systems Hybrid search, semantic retrieval, reranking, document ingestion and processing, vector databases such as Pinecone, Qdrant, Weaviate, pgvector, and Vertex AI Vector Search ...

New

Stellantis

AI Engineer - Supply Chain

Auburn Hills, MI · On-site

Experience with RAG, vector databases, or memory architecture * Familiarity with observability, evaluation frameworks, and safety guardrails * Experience integrating agents with ERP, WMS, TMS, or ...

TEKsystems

AI/ML Engineer

Dearborn, MI · On-site +1

$50 - $75/hr

RAG & Knowledge Systems Hybrid search, semantic retrieval, reranking, document ingestion and processing, vector databases such as Pinecone, Qdrant, Weaviate, pgvector, and Vertex AI Vector Search ...

Torc Robotics

Senior, ML Engineer - Auto Tagging

Ann Arbor, MI · On-site +1

$102K - $140K/yr

Experience building semantic retrieval systems or vector databases for automotive data. Perks of Being a Torc'r Torc cares about our team members and we strive to provide benefits and resources to ...

Torc Robotics

Senior, ML Engineer - Auto Tagging

Ann Arbor, MI · On-site +1

$102K - $140K/yr

Experience building semantic retrieval systems or vector databases for automotive data. Perks of Being a Torc'r Torc cares about our team members and we strive to provide benefits and resources to ...

X by 2

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 ...

THOMSON REUTERS

Principal Engineer

Ann Arbor, MI · On-site

Experience with AI-adjacent infrastructure: vector databases, embeddings, semantic search, or custom retrieval pipelines. * Opinions and experience around automated testing, reliability, and release ...

Centroid Systems, Inc.

OCI Architect

Troy, MI · Remote

Oracle Database 23ai: JSON Relational Duality, True Cache, AI Vector Search, Select AI * Database migration tools: MV2ADB, Data Pump, ZDLRA, Database Migration Service (DMS) Oracle Applications ...

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Showing results 41-60

All JobsVector Databases JobsVector Databases Jobs in Detroit, MI

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 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.

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 popular job titles related to Vector Databases jobs in Detroit, MI?

For Vector Databases jobs in Detroit, MI, the most frequently searched job titles are:

What job categories do people searching Vector Databases jobs in Detroit, MI look for?

The top searched job categories for Vector Databases jobs in Detroit, MI are:

What cities near Detroit, MI are hiring for Vector Databases jobs?

Cities near Detroit, MI with the most Vector Databases job openings:

Vector Databases jobs near you

AI Architect (with Azure)-Remote : Contract on w2

Marvel Technologies Inc

Southfield, MI • Remote

$65 - $84.75/hr

Contractor

Re-posted 28 days ago

Job description

Job Title :  AI Architect (with Azure)

Location :   Remote-USA

Duration : Long Term Contract

Contract on w2

Domain- Preferred Insurance.

Experience: 15+ years

Role Overview:

We are seeking a highly skilled AI Azure Architect to lead the architecture and technical strategy for AI programs across insurance and other regulated industries. The AI Architect will own and define reference architectures for Retrieval-Augmented Generation (RAG), Conversational AI, Document Intelligence, and Agentic AI, ensuring solutions are scalable, secure, compliant, and deliver measurable business value on AWS cloud/Azure Cloud.

Key Responsibilities:

  • Define end-to-end AI architectures covering ingestion → storage → retrieval → reasoning → action → monitoring.
  • Own and evolve reference architectures for Document AI, Conversational AI, and Agentic AI.
  • Specify non-functional requirements (latency, throughput, privacy, compliance, observability, cost).
  • Select and justify AWS-native AI/ML services (Bedrock, SageMaker, Kendra, OpenSearch, etc.) and third-party tools.

OR

  • Select and justify Azure-native AI/ML Services - Azure AI Foundry, Azure SDK, Cosmos DB, Azure OpenAI, Azure Blob Storage, Azure AI Search, Azure Cognitive Services, Service Principals, and Azure Agent (critical for agentic workflows).
  • Govern prompt/version management, enforce safety policies, and manage controls for prompt injection and PII protection.
  • Lead PoCs to production with AWS-based templates and golden paths.
  • Collaborate with stakeholders; mentor engineers; conduct design/code reviews.
  • Establish measurement frameworks (hallucination rate, groundedness, answer quality, CSAT, deflection).
  • Ensure seamless AWS/Azure enterprise integrations with insurance platforms (policy, claims, underwriting).

Required Skills & Experience:

  • 15+ years in AI/ML software, 3–5+ years in solution/enterprise architecture.
  • Proven experience designing AI systems at enterprise scale on AWS/Azure.
  • Hands-on with AWS Bedrock, SageMaker, Lambda, Kendra, OpenSearch, Redshift, DynamoDB, S3.

OR

  • Hands on Azure AI Foundry, Azure SDK, Cosmos DB, Azure OpenAI, Service Principals, Azure Blob, Azure AI Search, Azure Cognitive Services, and Azure Agent.
  • Expertise in LLMs, vector databases, RAG pipelines, and agentic workflows.
  • Strong multi-cloud cost/latency tradeoff knowledge.
  • Excellent communication, stakeholder engagement, and blueprinting skills.
  • Insurance industry experience strongly preferred (FNOL, claims adjudication, underwriting, billing, policy servicing).