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Vector Databases Jobs in Tennessee (NOW HIRING)

Ztek Consulting INC

AI/ML Engineer

Franklin, TN · On-site

... vector databases (Pinecone, Weaviate, pgvector) and graph databases (Neo4j). • Build and maintain ETL/ELT pipelines using AWS Glue or Apache Spark for data processing. • Ensure performance ...

C2 Labs, Inc

Principal AI Architect

Knoxville, TN · On-site

RAG and vector databases * LLM orchestration frameworks * Cloud-native architectures * Enterprise SaaS platforms * API integrations and workflow orchestration * OpenAI, Anthropic, Azure AI, AWS ...

C2 Labs, Inc

Principal AI Architect

Knoxville, TN

RAG and vector databases * LLM orchestration frameworks * Cloud-native architectures * Enterprise SaaS platforms * API integrations and workflow orchestration * OpenAI, Anthropic, Azure AI, AWS ...

CTI

AI Developer

Memphis, TN · On-site +1

Evaluate and integrate LLMs, embedding models, and vector databases to support efficient and accurate retrieval and generation. Design and implement scaffolding and orchestration around LLMs ...

CTI

Junior AI Developer

Memphis, TN · On-site +1

$60K - $78K/yr

Evaluate and integrate LLMs, embedding models, and vector databases to support efficient and accurate retrieval and generation. Design and implement scaffolding and orchestration around LLMs ...

CGI

Sr. Python Developer with AI

Knoxville, TN · On-site

$116K - $156K/yr

... databases, workflow orchestration services, and AI models. The platform leverages AWS Bedrock and vector based search technologies to help support teams quickly identify solutions, retrieve relevant ...

Matthews

Staff Software Engineer (TN)

Nashville, TN · On-site

$170K - $220K/yr

Experience with LLMs, LangChain/LangGraph, and vector databases Salary Range - $170k-220k depending on capability level and industry experience svg]:px-3 text-sm tracking-[0.025rem] leading-[1.5rem ...

AutoZone

Technical Program Manager

Memphis, TN · On-site

$125K - $162K/yr

... vector databases, and LLM-based retrieval systems is highly desirable About Us Since opening our first store in 1979, AutoZone has grown into a leading retailer and distributor of automotive parts ...

CGI Inc.

AI/ML Engineer

Knoxville, TN · On-site

RAG patterns; vector databases. o Web & APIs: HTML/CSS/JS; React or Angular; Node.js/Python/Java back ends; REST/GraphQL. o DevOps/MLOps: Docker, Kubernetes, Terraform, AWS CloudFormation; CI/CD with ...

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All JobsVector Databases JobsVector Databases Jobs in Tennessee

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- Systems Engineer

1 point system

Nashville, TN • On-site

Contractor

Posted 17 days ago

Job description

  • AI/ML Expertise: 8+ years of experience, with the last 2 years in GenAI technologies like OpenAI, Claude, Gemini, LangChain, Agents, Vector databases, Prompt Engineering, and fine-tuning.
  • Programming Languages & Libraries: Proficiency in Python, R, TensorFlow, PyTorch, Keras, Julia, and various ML libraries, along with NLP capabilities.
  • Web & Software Development: Skilled in Angular, React, NodeJS, Python, Streamlit, C#, .NET Core, Golang, SQL/NoSQL.
  • Cloud-Native Engineering: Experience with FaaS/PaaS/micro-services on cloud platforms like Azure, AWS, and GCP.
  • Methodologies & Tools: Strong understanding of XP, Lean, SAFe, DevSecOps, SRE, ADO, GitHub, SonarQube, and other tools for rapid, quality product delivery.