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Langgraph Jobs in Texas (NOW HIRING)

Droisys

Generative AI LangGraph experience

Plano, TX · On-site

Generative AI Engineer with LangGraph experience Plano, TX- Fully Onsite from Day-1 Core Technical Skills: * Python Programming: Advanced proficiency in Python, including experience with asynchronous ...

Salesforce

DS with GEN AI

Plano, TX · On-site

$14.25 - $18.50/hr

Hands-on experience building, deploying, and maintaining applications using LangChain and LangGraph frameworks. • Large Language Models (LLMs): In-depth understanding and practical experience with ...

IT America Inc

Agentic AI Engineer Lead

Dallas, TX · On-site

$101K - $133K/yr

Primary - LangGraph, ReAct, LangChain, LlamaIndex, Python Secondary - GCP, Google Spanner/Neo4j, CrewAI, AutoGen, OpenAI The Agentic AI Lead is a pivotal role responsible for driving the research ...

DRH Search

Sr. Product Engineer

Austin, TX · Hybrid

Building backend services and LangGraph agent workflows for document extraction and validation pipelines * Working directly with customers and SMEs to understand manual workflows and automate them ...

Vytwo

GenAI & Agentic AI Engineer

Prosper, TX · On-site

Develop Agentic AI workflows using frameworks such as CrewAI, AutoGen, LangGraph, or LangChain Agents . * Design and implement RAG pipelines , vector search solutions, and embedding‑based retrieval ...

Droisys

Generative AI Engineer

Dallas, TX · On-site

Build Generative AI solutions using technologies like LLMs, RAG, Prompt Engineering, and LangChain/LangGraph. * Develop AI Agents and Semantic Kernels for AI-driven enterprise automation solutions.

Wells Fargo

Senior Specialty AI Engineer

Irving, TX · Hybrid

$117K - $155K/yr

You will contribute to the development of LangChain/LangGraph-based workflows, RAG pipelines, and scalable services on Google Vertex AI. You will collaborate with senior engineers and cross ...

INFOSYS NOVA HOLDINGS LLC

Gen. AI Engineer

Fort Worth, TX · On-site

$100K - $160K/yr

Develop AI agents using frameworks such as LangChain, LangGraph, CrewAI, LlamaIndex, AutoGen, or similar technologies. * Create secure backend services and APIs using Python, FastAPI, Flask, or ...

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Langgraph information

What is the difference between Langgraph vs Data Analyst?

AspectLanggraphData Analyst
Required CredentialsTypically requires knowledge of language processing and graph databasesUsually requires a degree in statistics, mathematics, or related fields
Work EnvironmentTech companies, AI research labs, data-driven organizationsBusiness, finance, healthcare, and marketing sectors
Industry UsageEmerging role in AI and NLP projectsEstablished role in data interpretation and reporting

While Langgraph focuses on language processing and graph database integration, Data Analysts primarily interpret and visualize data to support business decisions. Both roles require analytical skills, but Langgraph specialists often have a background in AI and NLP, whereas Data Analysts typically hold degrees in statistics or related fields.

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

To thrive as a Langgraph engineer, you need a strong background in software engineering, proficiency in Python, and a solid understanding of AI/ML concepts, usually supported by a degree in computer science or a related field. Familiarity with machine learning frameworks (like TensorFlow or PyTorch), API integrations, and version control systems such as Git is essential. Effective problem-solving, collaboration, and clear communication are crucial soft skills for working with multidisciplinary teams and resolving complex issues. These capabilities are important because they enable the development, scaling, and maintenance of robust AI-driven applications using the Langgraph platform.

What is a Langgraph?

Langgraph is a framework designed to build, manage, and orchestrate complex workflows for large language models (LLMs). It allows developers to create directed graphs of language model prompts, tools, and custom logic, making it easier to design multi-step, stateful AI applications. Langgraph is especially useful for building conversational agents, automated workflows, and other applications that require LLMs to interact with data or tools in a structured way.

What are some common challenges faced by Langgraph developers when integrating their workflow with existing AI infrastructure?

Langgraph developers often encounter challenges when integrating their workflow with existing AI infrastructure, such as ensuring compatibility with various large language models and managing data flow across multiple APIs. Coordination with data engineers and machine learning specialists is crucial to align model outputs with business requirements, and adapting to rapidly evolving technologies can require continuous learning. Additionally, optimizing performance and maintaining security standards during integration are key considerations to ensure successful deployment.
What are popular job titles related to Langgraph jobs in Texas? For Langgraph jobs in Texas, the most frequently searched job titles are:
What job categories do people searching Langgraph jobs in Texas look for? The top searched job categories for Langgraph jobs in Texas are:
What cities in Texas are hiring for Langgraph jobs? Cities in Texas with the most Langgraph job openings:
Langgraph jobs near you
Infographic showing various Langgraph job openings in Texas as of July 2026, with employment types broken down into 56% Full Time, 6% Part Time, and 38% Contract. Highlights an 92% In-person, 3% Hybrid, and 5% Remote job distribution.

Java Developer - LangGraph

Texas State Library and Archives Commision

Dallas, TX • On-site

$47 - $61/hr

Full-time

Posted 26 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.

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