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Vector Databases Jobs in South Carolina (NOW HIRING)

Long Finch Technologies

ML-Ops / Platform Engineer

Tega Cay, SC · On-site

$46 - $63/hr

... vector databases, AI gateways, guardrails, and agentic frameworks. · Built and automated cloud-native ML infrastructure using Terraform, Kubernetes (EKS/AKS/OpenShift), Docker, GitHub Actions, Azure ...

New

Long Finch Technologies

ML-Ops / Platform Engineer

Lake Wylie, SC · On-site

$50.50 - $69.25/hr

... vector databases, AI gateways, guardrails, and agentic frameworks. · Built and automated cloud-native ML infrastructure using Terraform, Kubernetes (EKS/AKS/OpenShift), Docker, GitHub Actions, Azure ...

New

Long Finch Technologies

ML-Ops / Platform Engineer

Fort Mill, SC · On-site

$46.25 - $63.50/hr

... vector databases, AI gateways, guardrails, and agentic frameworks. · Built and automated cloud-native ML infrastructure using Terraform, Kubernetes (EKS/AKS/OpenShift), Docker, GitHub Actions, Azure ...

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LPL Financial LLC

Principal AI Architect

Fort Mill, SC · On-site

$156 - $260/hr

Evaluate and recommend enterprise technologies for foundation models, AI gateways, agent frameworks, MCP, vector databases, and AI governance platforms. * Drive the transition from siloed AI ...

LPL Financial

Principal AI Architect

Fort Mill, SC · On-site

Evaluate and recommend enterprise technologies for foundation models, AI gateways, agent frameworks, MCP, vector databases, and AI governance platforms. * Drive the transition from siloed AI ...

1 point system

Programming Engineer

Rock Hill, SC · On-site

Implement Retrieval-Augmented Generation (RAG) patterns combining vector databases or SQL Server VECTOR columns with LLM inference. * Evaluate AI-generated code critically: spot errors, security ...

LPL Financial

Principal AI Architect

Fort Mill, SC · On-site

Evaluate and recommend enterprise technologies for foundation models, AI gateways, agent frameworks, MCP, vector databases, and AI governance platforms. * Drive the transition from siloed AI ...

Cornerstone Defense LLC

SETA AI Engineer

Columbia, SC · On-site

$220 - $250/hr

Evaluate modern AI frameworks, agentic libraries, experimentation harnesses, vector databases, embeddings, inference pipelines, and cloud‑based AI environments for suitability in mission ...

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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 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 South Carolina?

For Vector Databases jobs in South Carolina, the most frequently searched job titles are:

What job categories do people searching Vector Databases jobs in South Carolina look for?

The top searched job categories for Vector Databases jobs in South Carolina are:

What cities in South Carolina are hiring for Vector Databases jobs?

Cities in South Carolina with the most Vector Databases job openings:

Vector Databases jobs near you

ML-Ops / Platform Engineer

Long Finch Technologies

Tega Cay, SC • On-site

$46 - $63/hr

Full-time

Posted yesterday

New

Job description

Must have skills: MLOps, AWS/Azure, Kubernetes, Docker, Python, Terraform, CI/CD, GenAI/LLM, RAG, Bedrock/Azure OpenAI, Vector DB, Observability Responsibilities:

·       Designed, deployed, and operated enterprise-grade MLOps/GenAI platforms across AWS and Azure, leveraging AWS Bedrock, SageMaker, Azure OpenAI, Azure AI Foundry, model serving, embeddings, RAG, vector databases, AI gateways, guardrails, and agentic frameworks.

·       Built and automated cloud-native ML infrastructure using Terraform, Kubernetes (EKS/AKS/OpenShift), Docker, GitHub Actions, Azure DevOps, Jenkins, GitOps, and ArgoCD, enabling scalable model deployment, CI/CD, versioning, and release management.

·       Implemented secure and highly available ML/AI platforms using AWS IAM, Azure IAM/RBAC, VPC/VNet, Key Vault, Secrets Manager, API Gateway, load balancers, ingress controllers, service mesh, autoscaling, and multi-account/subscription architectures.

·       Developed and operationalized ML/GenAI workloads using Python, REST APIs, microservices, MongoDB, PostgreSQL, Redis, and vector databases, implementing model evaluation, prompt engineering, state management, caching, and high-throughput inference capabilities.

·       Monitored, troubleshot, and optimized production ML/AI workloads using observability, logging, monitoring, SRE practices, performance tuning, resiliency, disaster recovery, and cost optimization, while collaborating with application, platform, infrastructure, and security teams.

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