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Vector Databases Jobs in Texas (NOW HIRING)

Redolent, Inc.

Senior Full Stack Engineer

Irving, TX · On-site

Experience with relational, NoSQL, and vector databases. Preferred Qualifications: * Experience with Azure Machine Learning, Azure Functions, Azure App Services, Azure DevOps, AWS Lambda, SageMaker ...

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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 Texas?

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

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

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

What cities in Texas are hiring for Vector Databases jobs?

Cities in Texas with the most Vector Databases job openings:

Vector Databases jobs near you
Infographic showing various Vector Databases job openings in Texas as of August 2026, with employment types broken down into 90% Full Time, 6% Part Time, and 4% Contract. Highlights an 81% Physical, 6% Hybrid, and 13% Remote job distribution.

AI Engineer with Python

Conflux Systems

Texas City, TX • On-site

Full-time

Posted 12 days ago

Job description

Job Title: AI Engineer
Locations: Plano, TX | Columbus, OH | Wilmington, DE
Job Summary
We are seeking an experienced AI Engineer to design, develop, and deploy AI-powered applications using modern machine learning and generative AI technologies. The ideal candidate has strong programming skills in Python and Java, along with hands-on experience building Agentic AI solutions using LLMs, orchestration frameworks, and cloud-native architectures.
Required Qualifications
  • Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, or a related field.
  • 5+ years of software engineering experience with a focus on AI/ML applications.
  • Strong proficiency in Python and Java.
  • Experience designing and implementing Agentic AI systems using Large Language Models (LLMs).
  • Hands-on experience with AI orchestration frameworks such as LangGraph, LangChain, AutoGen, CrewAI, or similar.
  • Experience integrating AI applications with REST APIs, databases, and enterprise systems.
  • Strong understanding of prompt engineering, Retrieval-Augmented Generation (RAG), embeddings, vector databases, and AI workflows.
  • Experience with cloud platforms such as AWS, Azure, or Google Cloud Platform.
  • Familiarity with containerization and orchestration technologies including Docker and Kubernetes.
  • Experience with Git, CI/CD pipelines, and Agile development methodologies.
  • Strong analytical, debugging, and problem-solving skills.
Preferred Qualifications
  • Experience working with OpenAI, Anthropic, Google Gemini, or open-source LLMs.
  • Knowledge of AI observability, evaluation, guardrails, and model monitoring.
  • Experience with vector databases such as Pinecone, Chroma, FAISS, or Milvus.
  • Familiarity with MLOps practices and model deployment pipelines.
  • Experience building conversational AI, AI assistants, or autonomous agents.
Key Responsibilities
  • Design and develop scalable AI applications using Python and Java.
  • Build and deploy Agentic AI solutions capable of planning, reasoning, and executing multi-step workflows.
  • Develop LLM-powered applications using prompt engineering, RAG, and vector search techniques.
  • Integrate AI services with enterprise applications, APIs, and data platforms.
  • Optimize AI model performance, latency, and cost.
  • Collaborate with product managers, data scientists, architects, and software engineers to deliver AI-driven solutions.
  • Implement testing, monitoring, security, and governance for AI applications.
  • Stay current with advancements in Generative AI, Agentic AI, and emerging AI technologies.

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