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Vector Databases Jobs in Lexington, SC (NOW HIRING)

United Airlines

AWS Generative AI Engineer

Columbia, SC · Hybrid

$60 - $78.75/hr

Vector Databases * AI Agents and Agentic Workflows * Finetuning and Model Evaluation * Responsible AI and AI Governance Programming Frameworks * Proficiency in Python * Experience with Lang Chain

Pwc

CTIO AI Engineering Manager

Columbia, SC

$73K - $244K/yr

... vector databases and orchestration tools like LangChain - Translating complex business problems into software-engineered AI solutions - Deploying on cloud platforms like AWS, GCP, Azure ...

Gap Inc.

Sr. ML Engineer - ML & Applied AI

Columbia, SC · On-site

$181K - $235K/yr

Contribute to the adoption of modern AI capabilities , including LLMs, vector databases, retrieval-augmented generation (RAG), and agentic workflows * Ensure high standards of code quality, testing ...

All JobsVector Databases JobsVector Databases Jobs in Lexington, SC

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.
What job categories do people searching Vector Databases jobs in Lexington, SC look for? The top searched job categories for Vector Databases jobs in Lexington, SC are:
What cities near Lexington, SC are hiring for Vector Databases jobs? Cities near Lexington, SC with the most Vector Databases job openings:
Vector Databases jobs near you

GenAI/ML Engineer | Generative AI (GenAI) Solutions Architect

MDAEdge

Columbia, SC • On-site

$58.25 - $76.75/hr

Full-time

Re-posted 29 days ago

Job description

Job Summary:
MDAEdge is seeking an experienced Generative AI Solutions Architect to lead the design and implementation of cutting-edge GenAI solutions. The role involves defining the architecture, leading development efforts, and ensuring scalable, ethical deployment of AI systems.
Responsibilities:
• Define end-to-end GenAI architecture, including model selection, fine-tuning, retrieval-augmented generation (RAG), vector databases, and prompt engineering pipelines.
• Design and deploy scalable software applications to support Generative AI initiatives.
• Build Minimum Viable Products (MVPs) for rapid iteration in dynamic environments.
• Hands-on model deployment from development to production, with troubleshooting and optimization.
• Collaborate with Data Scientists, MLOps, and Cloud Architects to ensure robust, compliant AI systems.
• Lead a small squad of engineers, providing technical guidance and fostering a high-performance culture.
• Mentor engineers of all levels and drive best practices in AI/ML development.
• Partner with Product, Legal, and Leadership to align AI solutions with ethical, regulatory, and business goals.
• Proactively resolve complex technical challenges across the AI/ML stack.
• Translate technical concepts for executives, engineers, and cross-functional teams.
Qualifications:
Required:
• Proven experience in GenAI architecture (RAG, vector stores, prompt engineering).
• Hands-on ML engineering skills: model training, deployment, and production troubleshooting.
• Expertise in Python and modern software development practices.
• Track record of delivering MVPs and scalable AI solutions.
• Strong leadership: ability to mentor engineers and lead technical teams.
Preferred:
• Familiarity with LLM fine-tuning (e.g., GPT, Llama, Claude).
• Experience with cloud platforms (AWS/Azure/GCP) and MLOps tools.
• Knowledge of AI ethics, compliance, and governance.
Company:
The world doesn't have a talent shortage. It has a talent alignment problem. MDA Edge exists to fix that. Founded in , the company is headquartered in Sheridan, WY, US, , with a team of 51-200 employees. The company is currently Growth Stage.

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