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Langgraph Jobs in Austin, TX (NOW HIRING)

YO AI Labs

AI/ML Engineer - Remote

Austin, TX · Remote

$200 - $350/hr

Develop and orchestrate multi-agent systems using LangGraph and LangChain . * Deploy AI solutions across secure cloud environments such as AWS GovCloud, Google GovCloud, Azure IL5+, Vertex AI, and ...

YO AI Labs

Mental Health Expert - Remote

Austin, TX · Remote

$200 - $350/hr

Develop and orchestrate multi-agent systems using LangGraph and LangChain . * Deploy AI solutions across secure cloud environments such as AWS GovCloud, Google GovCloud, Azure IL5+, Vertex AI, and ...

Datalinx AI

Full-Stack Engineer

Austin, TX · On-site

Responsibilities : • Design, develop, and maintain secure, scalable implementation of the Datalinx product. • Build AI-driven agentic solutions using frameworks like LangGraph and/or Autogen ...

Datalinx AI

Senior Full-Stack Engineer

Austin, TX · On-site

Responsibilities : • Design, develop, and maintain secure, scalable implementation of the Datalinx product. • Build AI-driven agentic solutions using frameworks like LangGraph, designing ...

Inorsa

AI Tech Lead

Austin, TX · On-site

Responsibilities : • Design and implement multi-actor state machines using LangGraph, including loops, error-recovery paths, and human-in-the-loop checkpoints • Own the observability and ...

3B Staffing LLC

AI/ML ENGINEER Role

Austin, TX · On-site

Advanced Agentic Systems: 2+ years building conversational assistants or autonomous agents using advanced techniques (LangGraph, CrewAI, A2A, CoT, ReAct, Reflection). * Adaptability: Demonstrated ...

3B Staffing LLC

AI/ML ENGINEER Role

Austin, TX · On-site

Advanced Agentic Systems: 2+ years building conversational assistants or autonomous agents using advanced techniques (LangGraph, CrewAI, A2A, CoT, ReAct, Reflection). * Adaptability: Demonstrated ...

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All JobsLanggraph JobsLanggraph Jobs in Austin, TX

Langgraph information

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 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 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 popular job titles related to Langgraph jobs in Austin, TX?

For Langgraph jobs in Austin, TX, the most frequently searched job titles are:

What job categories do people searching Langgraph jobs in Austin, TX look for?

The top searched job categories for Langgraph jobs in Austin, TX are:

What cities near Austin, TX are hiring for Langgraph jobs?

Cities near Austin, TX with the most Langgraph job openings:

Langgraph jobs near you
Infographic showing various Langgraph job openings in Austin, TX as of August 2026, with employment types broken down into 91% Full Time, 4% Part Time, and 5% Contract. Highlights an 82% Physical, 4% Hybrid, and 14% Remote job distribution.

AI Engineer (Python | Agentic AI | Vertex AI)

3B Staffing LLC

Austin, TX • On-site, Remote

Contractor

This job post has expired today. Applications are no longer accepted.

Job description

Job Title: AI Engineer (Python | Agentic AI | Vertex AI)
Location: Fully Remote (USA)
Duration: 6+ Months Contract

Python, LLM/Agentic AI, Google Vertex AI (or LangGraph/Bedrock), REST APIs, CLI scripting, pytest, structured logging, AI workflow orchestration, and telecom/RF domain experience preferred.
Job Description

We are seeking an experienced AI Engineer to join a high-impact AI transformation initiative for a leading telecommunications organization. In this role, you will build and enhance agentic AI systems that automate RF tower design and optimization workflows using modern LLM technologies. You will work on scalable AI agents, workflow orchestration, and production-grade Python applications while collaborating with cross-functional engineering teams.
Responsibilities

  • Design, develop, and enhance AI agent workflows using Python and modern LLM frameworks.
  • Extend and maintain existing AI agent codebases without creating redundant services.
  • Build AI-driven automation for RF engineering workflows and design optimization.
  • Develop and maintain APIs, CLI integrations, and workflow orchestration.
  • Write unit, integration, and regression tests using pytest.
  • Implement structured logging, monitoring, and debugging for AI workflows.
  • Collaborate with engineering, product, and telecom domain experts throughout the SDLC.
  • Support pilot deployments, production rollouts, and post-production monitoring.
  • Optimize AI agent performance, scalability, and reliability.

Required Skills

  • 4-7+ years of hands-on Python development experience.
  • At least 1 year of production experience with LLMs, Agentic AI, or Generative AI applications.
  • Experience with Google Vertex AI or similar frameworks such as LangGraph, Agent Builder, Bedrock Agents, or comparable AI orchestration platforms.
  • Strong knowledge of REST APIs, CLI tools, scripting, and automation.
  • Experience working with distributed systems and AI workflow orchestration.
  • Strong debugging, structured logging, and testing experience.
  • Experience with pytest, unit testing, integration testing, and regression testing.
  • Excellent problem-solving and communication skills.

Preferred Skills

  • Experience contributing to MCP servers or extending existing AI platforms.
  • Knowledge of RF engineering, telecommunications, LTE, 5G NR, or wireless network technologies.
  • Familiarity with AI evaluation frameworks and production deployment best practices.
  • Experience working in Agile/Scrum environments.

Nice to Have

  • Experience with LangChain, LangGraph, OpenAI APIs, Vertex AI, or Bedrock.
  • Cloud experience with GCP or AWS.
  • Background in telecom infrastructure or network optimization.

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