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Langgraph Jobs in Ohio (NOW HIRING)

LangGraph * vLLM * Experience designing and orchestrating multi-agent workflows, tool integrations, and autonomous decision-making frameworks. * Ability to develop, test, and optimize AI-driven ...

Experience with LangChain, LangGraph, NVIDIA NIM, or Hugging Face * Experience leading AI or ERP transformation programs for large enterprises The wage range for this role takes into account the wide ...

Experience with LangChain, LangGraph, NVIDIA NIM, or Hugging Face * Experience leading AI or ERP transformation programs for large enterprises The wage range for this role takes into account the wide ...

Experience with LangChain, LangGraph, NVIDIA NIM, or Hugging Face * Experience leading AI or ERP transformation programs for large enterprises The wage range for this role takes into account the wide ...

Python - Advanced

Columbus, OH · On-site

$75 - $90/hr

Java-strong engineers with real agentic delivery are rare - the Python + Java + LangGraph + vLLM intersection is a unicorn profile. Market data shows generalist/architect listings pay more than ...

Build agent harnesses in Python using LangChain and LangGraph, including tool-calling, structured outputs (Pydantic/JSON schema), retries, streaming, and memory * Package agent harnesses for the ...

Senior Java Developer - GenAI

Columbus, OH · On-site

$53.50 - $68.25/hr

LangChain, LangGraph, LlamaIndex, or similar GenAI frameworks. * Vector databases such as Pinecone, Weaviate, Milvus, pgvector, or similar. * Experience building Agentic AI / Multi-Agent Systems

Sr Software Engineer

Columbus, OH · On-site

$114K - $150K/yr

LangChain, LangGraph, Agent Core * ML lifecycle + data engineering * API and system integration * Cloud (AWS / Azure AI stack) * Resume Review * Manager will review resumes in batches & will be ...

Java Architect

Columbus, OH · On-site

$58.75 - $79.50/hr

LangChain, LangGraph, LlamaIndex, or similar GenAI frameworks. Vector databases such as Pinecone, Weaviate, Milvus, pgvector, or similar. Experience building Agentic AI / Multi-Agent Systems.

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Showing results 21-40

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 cities in Ohio are hiring for Langgraph jobs?

Cities in Ohio with the most Langgraph job openings:

Infographic showing various Langgraph job openings in Ohio as of August 2026, with employment types broken down into 1% Internship, 93% Full Time, 1% Part Time, and 5% Contract. Highlights an 77% Physical, 6% Hybrid, and 17% Remote job distribution.

Agentic AI/Java/Python

Judge Group, Inc.

Columbus, OH • On-site

Other

Posted 6 days ago


Job description

Location: Columbus, OH Description:
Agentic AI Consultant
Plano, TX / Columbus, OH
6+months Contract TO Perm

Job Description
Required Skills / Must Have
  1. Strong proficiency in Python and Java, with hands-on experience developing and supporting production-grade applications.
  2. Demonstrated experience building, deploying, and maintaining Agentic AI solutions in enterprise environments.
  3. Hands-on experience with the following technologies:
    1. Python
    2. Java
    3. LangGraph
    4. vLLM
  4. Experience designing and orchestrating multi-agent workflows, tool integrations, and autonomous decision-making frameworks.
  5. Ability to develop, test, and optimize AI-driven applications that leverage large language models, agent frameworks, and retrieval capabilities.
  6. Strong understanding of API development, microservices architectures, and integration patterns within modern software ecosystems.
  7. Experience troubleshooting, monitoring, and supporting AI-enabled applications in production environments.

Familiarity with responsible AI practices, model governance, security considerations, and enterprise development standards
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