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Vector Databases Jobs (NOW HIRING)

Actian Corporation

Vector Database Senior Sales Engineer

OR · On-site +1

$105K - $143K/yr

Candidate brings deep expertise in vector databases (Milvus, Qdrant, and other vectorized databases as they emerge), RAG architectures, and LangChain integrations, combined with broad experience in ...

Actian Corporation

Vector Database Senior Sales Engineer

$108K - $147K/yr

Candidate brings deep expertise in vector databases (Milvus, Qdrant, and other vectorized databases as they emerge), RAG architectures, and LangChain integrations, combined with broad experience in ...

Actian Corporation

OR · Hybrid

$105K - $143K/yr

Candidate brings deep expertise in vector databases (Milvus, Qdrant, and other vectorized databases as they emerge), RAG architectures, and LangChain integrations, combined with broad experience in ...

Asterism IT Solutions

Database Architect

Austin, TX · On-site

$140 - $190/hr

Event-driven integration * 2+ years of experience with vector databases and/or feature stores is preferred. * 2+ years of experience with Python scripting is preferred. * 2+ years of experience ...

ClifyX

Python AI/GenAI developer

Denver, CO · On-site

$51.75 - $71.25/hr

The role involves working with Google technologies, local LLMs, vector databases, and open-source frameworks to enhance MLOps data models. Responsibilities : • Exposure to Google technologies ...

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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.

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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 89% Full Time, 6% Part Time, and 5% Contract. Highlights an 81% Physical, 6% Hybrid, and 13% Remote job distribution.

Data AI Engineer with Vector Databases

Apex Informatics

Plano, TX • On-site

$109K - $131K/yr

Other

Re-posted 25 days ago

Job description

Title: Data AI Engineer with Vector Databases
Location: Plano, TX
Job Description:
Key Responsibilities:
  1. Design and build ETL/ELT pipelines and data processing workflows
  2. Develop batch and real-time data pipelines using modern frameworks
  3. Work with Python and SQL for data transformation and analytics
  4. Implement GenAI data architectures, including RAG pipelines and vector indexing
  5. Manage and optimize Vector Databases for embedding storage and similarity search
  6. Build secure data solutions on AWS, ensuring data quality and compliance
  7. Support analytics, reporting, and data modernization initiatives

Required Skills:
  1. Strong experience in Python and SQL
  2. Hands-on experience with ETL/ELT and data pipelines
  3. Mandatory: Experience with Vector Databases
  4. Experience with GenAI / LLM frameworks (LangChain or LangGraph)
  5. Experience with Big Data frameworks (Apache Spark, Apache Kafka)
  6. Workflow orchestration using Apache Airflow
  7. Experience with data platforms like Databricks or Snowflake
  8. AWS services: S3, Glue, Redshift

Nice to Have:
  1. Experience with ML frameworks (Scikit-learn, PyTorch)
  2. Knowledge of RAG architectures and embedding pipelines
  3. Experience in financial services / fintech environments

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