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Langgraph Jobs in Georgia (NOW HIRING)

Senior AI Engineer

Alpharetta, GA · On-site

$119K - $157K/yr

Agentic AI, RAG, LangChain/LangGraph, or Talk-to-Data solutions. * Docker, Kubernetes, Kafka, Redis. * OpenTelemetry, Grafana, Prometheus. * CI/CD (Jenkins, GitHub Actions), DevOps/GitOps.

Senior AI Engineer

Alpharetta, GA · On-site

$119K - $157K/yr

Agentic AI, RAG, LangChain/LangGraph, or Talk-to-Data solutions. * Docker, Kubernetes, Kafka, Redis. * OpenTelemetry, Grafana, Prometheus. * CI/CD (Jenkins, GitHub Actions), DevOps/GitOps.

AI/ML Quality Engineer

Atlanta, GA · On-site

$69K - $89K/yr

Must have hands-on with GenAI frameworks (LangChain, LlamaIndex, LangGraph, Neo4j, Bedrock, etc.). * Hands-on experience implementing the RAG pipelines - Knowledge Management with vector databases ...

Senior AI Engineer

Alpharetta, GA · On-site

$119K - $157K/yr

Agentic AI, RAG, LangChain/LangGraph, or Talk-to-Data solutions. * Docker, Kubernetes, Kafka, Redis. * OpenTelemetry, Grafana, Prometheus. * CI/CD (Jenkins, GitHub Actions), DevOps/GitOps.

Senior AI Engineer

Alpharetta, GA · On-site

$119K - $157K/yr

Agentic AI, RAG, LangChain/LangGraph, or Talk-to-Data solutions. * Docker, Kubernetes, Kafka, Redis. * OpenTelemetry, Grafana, Prometheus. * CI/CD (Jenkins, GitHub Actions), DevOps/GitOps.

... LangGraph, CrewAI, OpenAI agents, or similar frameworks. • Experience with multi-modal data and intelligent agent-based tools. • Ability to develop prototypes, PoCs, MVPs using one or more of the ...

AI/ML Engineer

Atlanta, GA · On-site

$50 - $70/hr

Experience developing AI agents or agentic workflows using frameworks such as Lang Chain, LangGraph, CrewAI, AutoGen, or similar. Experience implementing end-to-end AI solutions that automate ...

Automation Engineer

Atlanta, GA · On-site

$120 - $190/hr

Leverage agentic frameworks (e.g., LangChain, LangGraph, LlamaIndex, Semantic Kernel, AutoGen, CrewAI, Microsoft Agent Framework, or similar) to orchestrate LLM-powered workflows.Design and implement ...

New

Lead AI Engineer

Atlanta, GA · On-site

$96K - $181K/yr

Apply agentic AI patterns utilizing Gemini, LangGraph, LangChain, and emerging AI frameworks to enhance conversational experiences. * Develop, test, evaluate, and optimize prompts, workflows, and AI ...

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 Georgia are hiring for Langgraph jobs?

Cities in Georgia with the most Langgraph job openings:

Infographic showing various Langgraph job openings in Georgia as of August 2026, with employment types broken down into 1% Internship, 86% Full Time, 3% Part Time, and 10% Contract. Highlights an 77% Physical, 6% Hybrid, and 17% Remote job distribution.

Agentic AI / Semantic Solutions Architect

Staffingine LLC

Atlanta, GA • On-site

Contractor

Re-posted 9 days ago


Job description

Job Title: Agentic AI / Semantic Solutions Architect
Job Location:
Atlanta, Georgia, USA
Job Type: Contract

Job Description:

  • Architect and design agentic AI workflows that consume outputs from semantic layers, including knowledge graphs, ontologies, and metadata catalogs
  • Develop and prototype GraphRAG pipelines that combine graph traversal with vector-based retrieval for accurate, domain-grounded responses
  • Define and implement context engineering strategies, including metadata injection, chunking, and semantic optimization for LLM prompts
  • Design and build Model Context Protocol (MCP) server patterns to enable seamless interaction between agents and semantic data systems
  • Develop LLM orchestration workflows using frameworks such as LangChain, LangGraph, LlamaIndex, or AutoGen
  • Build pipelines for automated metadata extraction and semantic tagging using NLP and LLM-based approaches
  • Collaborate with Semantic Data Architects to ensure ontologies and graph structures are optimized for agent traversal and querying
  • Prototype agent-based solutions for business use cases such as:
    • Credit risk analysis
    • Customer data onboarding workflows