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Graph Jobs in Texas (NOW HIRING)

Explore and apply AI/ML techniques, including Large Language Models (LLMs), generative AI (GenAI), and Graph Neural Networks (GNNs), to geometry, mesh, and graph-structured engineering data.

If you are passionate about big data, graph modeling, and cybersecurity€¿and love turning messy data into clean, actionable intelligence€¿this is the role for you. What You'll Do: • Own ...

AI Engineer

Fort Worth, TX · On-site

$114K - $150K/yr

Implement Graph RAG solutions by integrating knowledge graphs, entity extraction, relationship mapping, graph traversal, and contextual retrieval. * Develop multi-agent systems using frameworks such ...

Perform M365 integrations using Graph API and other services. * Conduct troubleshooting, testing, and optimization of developed solutions. Required Qualifications * Bachelor's or Master's degree.

Perform M365 integrations using Graph API and other services. * Conduct troubleshooting, testing, and optimization of developed solutions. Required Qualifications * Bachelor's or Master's degree.

Deep knowledge of logic and proof techniques, set theory, combinatorics, graph theory, number theory, recurrence relations, Boolean algebra, algorithms, and formal languages. Ability to explain ...

Deep knowledge of logic and proof techniques, set theory, combinatorics, graph theory, number theory, recurrence relations, Boolean algebra, algorithms, and formal languages. Ability to explain ...

Showing results 41-60

Graph information

See Texas salary details

$8

$28

$111

How much do graph jobs pay per hour?

As of Sep 15, 2026, the average hourly pay for graph in Texas is $28.91, according to ZipRecruiter salary data. Most workers in this role earn between $14.76 and $24.18 per hour, depending on experience, location, and employer.

What is a graph?

A Graph job typically refers to a position involving the analysis, visualization, or implementation of graph-based data structures and algorithms. This may include working with graph databases, network analysis, or machine learning applications that leverage graph theory. Common roles include Graph Data Scientist, Graph Engineer, or Network Analyst, often requiring expertise in tools like Neo4j, GraphQL, or NetworkX. These jobs are commonly found in industries such as social networks, cybersecurity, recommendation systems, and logistics.

How does a graph database engineer typically collaborate with data scientists and software developers in a project setting?

Graph Database Engineers often work closely with data scientists to design and optimize data models that support complex relationships and queries. They collaborate with software developers to integrate graph databases into applications, ensuring seamless data flow and performance. Regular meetings and code reviews help align database structures with business requirements and analytical goals. This cross-functional teamwork is essential for delivering scalable, high-performing solutions that leverage graph-based data.

What are the key skills and qualifications needed to thrive as a graphic designer, and why are they important?

To thrive as a Graphic Designer, you need a strong foundation in design principles, creativity, and proficiency in visual communication, often supported by a degree in graphic design or a related field. Mastery of technical tools such as Adobe Creative Suite (Photoshop, Illustrator, InDesign) and knowledge of digital asset management systems are typically required. Excellent communication, time management, and collaboration skills help designers effectively convey ideas and work with clients or teams. These skills are essential to producing compelling visuals that meet client goals and stand out in a competitive creative industry.

What is the difference between Graph vs Data Analyst?

AspectGraphData Analyst
Required CredentialsTypically no formal degree, but knowledge of graph theory helpsBachelor's or higher in data science, statistics, or related fields
Work EnvironmentResearch, academia, or specialized tech rolesBusiness, finance, healthcare, and various industries
Employer & Industry UsageUsed in computer science, mathematics, and research projectsApplied in analyzing data trends, reporting, and decision-making

While a graph refers to a mathematical or visual representation of data, a Data Analyst is a professional who interprets data, often using graphs as tools. The Data Analyst's role involves analyzing data sets, creating visualizations, and providing insights, whereas a graph is a component or tool used within data analysis processes.

What careers use graphs?

Careers that use graphs include data analysts, statisticians, data scientists, and business analysts, who interpret and visualize data to support decision-making. These roles often require skills in data visualization tools like Excel, Tableau, or Power BI, and involve analyzing trends, patterns, and relationships within data sets.
Infographic showing various Graph job openings in Texas as of August 2026, with employment types broken down into 69% Full Time, 25% Part Time, 5% Contract, and 1% Nights. Highlights an 54% Physical, 8% Hybrid, and 38% Remote job distribution, with an average salary of $60,138 per year, or $28.9 per hour.

Java Developer - LangGraph

Dallas, TX • On-site

Texas State Library and Archives Commision
Libraries and Archives • 201 - 500 employees

$47 - $61/hr

Full-time

Re-posted 17 days ago


Job description

We are seeking a skilled Java Developer with hands-on experience in LangGraph to join our growing engineering team. You will be responsible for developing, integrating, and maintaining high-performance applications that utilize language models within a graph-based computational workflow. This role offers a unique opportunity to work at the intersection of AI, distributed systems, and backend Java development.
Key Responsibilities:
  • Design, develop, and maintain Java-based backend services and APIs.
  • Integrate and manage LangGraph workflows, leveraging LLMs to build dynamic, branching applications.
  • Collaborate with AI engineers to integrate language models (e.g., OpenAI, Hugging Face) into existing infrastructure.
  • Optimize performance and scalability of graph-based systems for production environments.
  • Write clean, maintainable, and testable code.