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Pinecone Vector Databases Jobs in Detroit, MI (NOW HIRING)

Kla

AI Software Engineer|Manufacturing

Ann Arbor, MI

$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|Manufacturing

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 ...

X by 2

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 ...

Deloitte

Cyber - SecOps - Consultant

Detroit, MI

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 ...

All JobsPinecone Vector Databases JobsPinecone Vector Databases Jobs in Detroit, MI

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.

What are popular job titles related to Pinecone Vector Databases jobs in Detroit, MI? For Pinecone Vector Databases jobs in Detroit, MI, the most frequently searched job titles are:
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What cities near Detroit, MI are hiring for Pinecone Vector Databases jobs? Cities near Detroit, MI with the most Pinecone Vector Databases job openings:
Pinecone Vector Databases jobs near you

Agentic AI Engineer -Auburn Hills, MI-2 days onsite : Contract on w2

Marvel Technologies Inc

Auburn Hills, MI • On-site

$58 - $62/hr

Contractor

Re-posted 23 days ago

Job description

Agentic AI/ML Engineer -Hybrid (Only w2 consultants)
Auburn Hills, MI-2 days onsite
12 months
 
Need 10+ plus years experience
 

We are seeking a highly skilled Senior Agentic AI Engineer to join our team and drive the development of cutting-edge autonomous AI systems. This role focuses on building sophisticated AI agents that can reason, plan, and execute complex tasks with minimal human intervention.
 
## Position Summary
As a Senior Agentic AI Engineer, you will design and implement intelligent agent systems using state-of-the-art frameworks and technologies. You'll work at the intersection of large language models, knowledge graphs, and distributed systems to create AI solutions that can autonomously handle complex workflows and decision-making processes.
 
## Key Responsibilities
**Agent Development & Architecture**
- Design and implement multi-agent systems using LangGraph for complex workflow orchestration
- Build autonomous AI agents capable of planning, reasoning, and task execution
- Develop agent communication protocols and coordination mechanisms
- Create robust error handling and recovery systems for agent workflows
 
**AI Infrastructure & Integration**
- Implement and optimize LangChain pipelines for agent reasoning and tool usage
- Design and maintain vector database solutions for knowledge retrieval and semantic search
- Build scalable data ingestion pipelines for structured and unstructured data
- Integrate Google Cloud AI services including Vertex AI, Gemini, and PaLM models
 
**Backend Development & APIs**
- Develop high-performance APIs using FastAPI for agent interaction and monitoring
- Implement real-time streaming capabilities for agent responses and status updates
- Build monitoring and observability systems for agent performance tracking
- Create robust authentication and authorization systems for agent access
**Data & Knowledge Management**
- Design and implement RAG (Retrieval-Augmented Generation) systems
- Optimize vector embeddings and similarity search algorithms
- Build knowledge graph integration for enhanced agent reasoning
- Implement efficient caching and data persistence strategies
 
## Required Technical Skills
**Core AI/ML Frameworks**
- Expert-level proficiency with LangGraph for agent workflow orchestration
- Deep experience with LangChain for LLM application development
- Hands-on experience with Google Cloud AI services (Vertex AI, Gemini, PaLM)
- Strong understanding of prompt engineering and LLM optimization techniques
**Data & Storage**
- Proficiency with vector databases (Pinecone, Weaviate, Chroma, or similar)
- Experience with traditional databases (PostgreSQL, MongoDB)
- Knowledge of data ingestion frameworks and ETL pipelines
- Understanding of embedding models and semantic search optimization
**Python Development**
- Advanced Python programming skills with 10+ years of experience
- Expert-level FastAPI development for building scalable APIs
- Proficiency with async/await patterns and concurrent programming
- Experience with Python ML libraries (NumPy, Pandas, scikit-learn)
- Knowledge of testing frameworks (pytest, unittest) and CI/CD practices
**Cloud & Infrastructure**
- Hands-on experience with Google Cloud Platform (GCP)
- Proficiency with containerization (Docker) and orchestration (Kubernetes)
- Experience with cloud storage solutions and data pipelines
- Understanding of microservices architecture and distributed systems
## Preferred Qualifications
 
**Additional Technical Skills**
- Experience with other agent frameworks (AutoGPT, CrewAI, TaskWeaver)
- Knowledge of graph databases (Neo4j, Amazon Neptune)
- Familiarity with streaming frameworks (Apache Kafka, Redis Streams)
- Experience with monitoring tools (Prometheus, Grafana, LangSmith)
**AI/ML Expertise**
- Understanding of transformer architectures and attention mechanisms
- Experience with fine-tuning and adapting large language models
- Knowledge of reinforcement learning for agent training
- Familiarity with multi-modal AI systems (vision, text, audio)
**Development Experience**
- Experience with frontend frameworks (React, Vue.js) for agent interfaces
- Knowledge of WebSocket programming for real-time applications
- Familiarity with message queues and event-driven architectures
- Experience with performance optimization and scalability challenges