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Vector Databases Jobs in Concord, NC (NOW HIRING)

Stellent IT LLC

Sr. Data Scientist

Charlotte, NC · On-site

Implement and support vector database solutions for semantic search and RAG architecture . Collaborate with DevOps and platform teams to implement CI/CD pipelines for ML, GenAI, and data workloads.

BC Forward

Full Stack AI Developer

Charlotte, NC · On-site

$89.73/hr

Integrate relational and vector databases to support application and AI workflows. Collaborate with AI engineers to integrate models and agentic workflows. Ensure secure, scalable, and maintainable ...

TCS USAAvance Consulting

Python Gen AI

Charlotte, NC · On-site

$49 - $67.75/hr

... with vector databases for embeddings and semantic search o Ensure data privacy, security, and responsible AI practices o Collaborate with product managers, UX designers, and data scientists o ...

GDIT

AI Platform Engineer

Concord, NC · Hybrid

$127K - $172K/yr

Integrate vector databases and knowledge repositories to support RAG and graph-augmented LLM workflows. * Build and maintain secure REST APIs for AI job submission, inference requests, and workflow ...

AceStack LLC

Python + GenAI

Charlotte, NC · On-site

$49 - $67.75/hr

... vector databases for embeddings and semantic search • Ensure data privacy, security, and responsible AI practices • Collaborate with product managers, UX designers, and data scientists • ...

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All JobsVector Databases JobsVector Databases Jobs in Concord, NC

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 Concord, NC look for? The top searched job categories for Vector Databases jobs in Concord, NC are:
What cities near Concord, NC are hiring for Vector Databases jobs? Cities near Concord, NC with the most Vector Databases job openings:
Vector Databases jobs near you
Infographic showing various Vector Databases job openings in Concord, NC as of June 2026, with employment types broken down into 63% Full Time, 26% Part Time, and 11% Contract. Highlights an 68% Physical, 3% Hybrid, and 29% Remote job distribution.

Sr. Data Scientist - Charlotte, NC - Oniste

Gandiva Insights

Charlotte, NC • On-site

Contractor

Posted 3 days ago

Key responsibilities

  • Design, develop, and optimize scalable ETL/ELT data pipelines for structured and unstructured data using Databricks, Spark, SQL, and AWS services.

  • Design, implement, and deploy scalable machine learning and Generative AI models, including model evaluation, tuning, validation, and integration with enterprise knowledge sources.

  • Deploy and manage ML and GenAI models on AWS SageMaker and Databricks, implement CI/CD pipelines, and automate operational workflows for scalability and reliability.

Job description

W2 only [No C2C]

Position : Sr. Data Scientist

Location: Charlotte, NC [Hybrid (4 days onsite and 1 day remote)]

Duration: 6 Months Contract 

Job Description: 

Data Engineering & Data Processing:

  • Design and develop scalable ETL/ELT pipelines for ingesting, transforming, and processing structured and unstructured data.
  • Build and optimize data pipelines using Databricks, Spark, SQL, and cloud-native AWS services.
  • Implement data quality, validation, lineage, and monitoring processes.
  • Support medallion/Lakehouse architecture patterns including bronze, silver, and gold data layers.
  • Develop data pipelines to support AI/ML, GenAI, and RAG workloads, including document ingestion and embedding generation workflows.

Machine Learning & Modeling:

  • Design and implement scalable ML models for classification, regression, clustering, forecasting, and recommendation systems.
  • Apply advanced techniques including deep learning, ensemble learning, NLP, Generative AI, and LLM-based solutions where applicable.
  • Conduct model evaluation, tuning, validation, and performance optimization using industry best practices.
  • Develop and train models within Databricks ML and/or AWS SageMaker leveraging distributed computing and scalable cloud infrastructure.
  • Build reusable feature engineering and model training pipelines.
  • Develop Retrieval-Augmented Generation (RAG) solutions integrating LLMs with enterprise knowledge sources and vector databases.

Cloud & MLOps:

  • Deploy and manage ML and GenAI models using AWS SageMaker and Databricks, including endpoint configuration, monitoring, and retraining workflows.
  • Utilize Databricks MLflow for experiment tracking, model registry, and deployment automation.
  • Implement and support vector database solutions for semantic search and RAG architecture.
  • Collaborate with DevOps and platform teams to implement CI/CD pipelines for ML, GenAI, and data workloads.
  • Automate operational workflows and optimize cloud resource utilization, scalability, reliability, and security.

Deliverables:

  • Production-ready ML and GenAI solutions with supporting technical documentation.
  • Scalable ETL/ELT pipelines and curated datasets.
  • End-to-end Databricks notebooks, jobs, and workflows.
  • Feature engineering pipelines and reusable ML components.
  • RAG pipelines integrated with vector databases and enterprise knowledge sources.
  • Weekly status reports and participation in Agile sprint ceremonies.

Skills & Qualifications:

  • 8+ years of experience in Data Science, Machine Learning, and Data Engineering.
  • Strong proficiency in Python, SQL, Spark, and ML libraries such as scikit-learn, TensorFlow, and PyTorch.
  • Experience with Generative AI, LLM frameworks, prompt engineering, and RAG architecture.
  • Hands-on experience with vector databases and semantic search technologies.
  • Hands-on experience with Databricks, MLflow, Delta Lake, and AWS SageMaker.
  • Experience designing scalable data pipelines and distributed data processing solutions.
  • Strong understanding of data mining, feature engineering, and data modeling techniques.
  • Experience with cloud-native AWS data services and orchestration frameworks.
  • Excellent communication, collaboration, and leadership skills.