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Vector Databases Jobs (NOW HIRING)

CCS INC

AI/ML Engineer

Plano, TX · On-site

$65 - $75/hr

Production experience with vector databases and designing ingestion + embedding pipelines for both batch and streaming workloads. * Hands-on with prompt design, evaluation, LLM orchestration, and RAG ...

Wise Equation Solutions Inc.

AI/ML Engineer

Dallas, TX · On-site

The ideal candidate will have hands-on experience building applications using LLMs, vector databases, and agent harnesses, along with experience in Azure stack. This role involves developing scalable ...

Alpha Consulting Corp.

Gen AI Architect

Mclean, VA · On-site

$63.75 - $84/hr

... vector databases, and cloud deployments. • Implement Responsible AI techniques, including strategy and execution. Qualifications : Required : • AI Architect- Create overarching solution ...

Scalence L.L.C.

Gen AI Architect

Mclean, VA · On-site

$63.75 - $84/hr

... vector databases, and cloud deployments. • Implement Responsible AI techniques, including strategy and execution. Qualifications : Required : • AI Architect- Create overarching solution ...

Robustware

AI/ML Engineer

Cupertino, CA · On-site

* Build and optimize RAG pipelines, vector databases, embeddings, and document-processing workflows * Design agentic AI systems -- including tool calling, orchestration, reasoning loops, and workflow ...

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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 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
Infographic showing various Vector Databases job openings in the United States as of August 2026, with employment types broken down into 87% Full Time, 5% Part Time, 1% Temporary, and 7% Contract. Highlights an 84% Physical, 5% Hybrid, and 11% Remote job distribution.

AI/ML Engineer

CCS INC

Plano, TX • On-site

$65 - $75/hr

Full-time

Medical, Dental, Vision

Re-posted 16 days ago

Job description

Benefits:
  • Bonus based on performance
  • Dental insurance
  • Health insurance
  • Vision insurance

Qualifications
  • Bachelor’s Degree 
  • 6+ years cloud architecture experience
  • 3+ years building production GenAI/LLM systems on AWS.  
  • Strong Python and AWS expertise, including Lambda, ECS/EKS, S3, SageMaker, Docker and Kubernetes.  
  • Production experience with vector databases and designing ingestion + embedding pipelines for both batch and streaming workloads.  
  • Hands-on with prompt design, evaluation, LLM orchestration, and RAG implementation patterns.  
  • Experience deploying and operating model- serving or MCP – like server infrastructure (selfhosted or managed).  
  • Proficient with IaC and delivery tooling, including Terraform/CloudFormation, GitOps, and CI pipelines.  
  • Experience with model-serving infrastructure, such as Amazon SageMaker, NVIDIA Triton, Ray Serve, or similar platforms.  
  • Hands-on experience with GenAI libraries and frameworks, including LangChain, LlamaIndex, Hugging Face, and OpenAI APIs.  
  • Deep operational expertise with vector databases, such as Pinecone, Milvus, Weaviate, or Qdrant.  
  • AWS Solutions Architect, AWS DevOps Engineer, or equivalent industry certifications. 
 
Responsibilities
  • Cloud Architecture & Infrastructure, Design scalable, secure AWS architectures
  • LLM & GenAI Platforms, Lead integration of API-based and self-hosted LLMs, implement RAG solutions
  • Prompting & Evaluation, Develop prompt engineering strategies, reusable templates, and evaluation frameworks 
  • Vector Databases & Retrieval Pipelines, Implement and maintain vector stores (OpenSearch, Pinecone, Milvus, Qdrant) 
  • Data Ingestion & Processing Pipelines 
  • Microservices & Serverless Systems 
  • Python Development & AI Tooling 
  • Security, Governance & Cross-Functional Leadership