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Langgraph Jobs in California (NOW HIRING)

Founding AI Engineer

San Francisco, CA · On-site

$140 - $190/hr

Build AI systems with LangGraph: Design, build, and maintain scalable AI systems with LangGraph and TypeScript to create automated marketing campaigns. * Design Data Backbone: Work on the data ...

New

Hands-on experience with LangGraph for workflow orchestration * Expertise in building Retrieval-Augmented Generation (RAG) pipelines * Knowledge of semantic layers and knowledge graphs for structured ...

Design and deploy production-grade agents (using LangGraph and LangSmith) that handle technical support queries, troubleshoot integrations, and guide users through complex onboarding flows. • Drive ...

AI Engineer

Newark, CA · On-site

$80 - $90/hr

The ideal candidate will have deep expertise in Python , practical experience with LangGraph/LangChain for agent workflows, and proficiency in FastAPI for developing production-grade APIs. This role ...

Hands-on experience with LangGraph, LangChain, Large Language Models (LLMs), and prompt engineering. * Experience architecting, designing, and deploying enterprise-grade AI applications. * Strong ...

Design and deploy production-grade agents (using LangGraph and LangSmith) that handle technical support queries, troubleshoot integrations, and guide users through complex onboarding flows. • Drive ...

Experience with agent frameworks such as Google ADK Langgraph CrewAI etc * Hands on experience in working with cloud AI services GCP Vertex AI Azure AI AWS Bedrock as well as managed large language ...

AI Software Engineer

San Jose, CA · On-site

$102 - $107/hr

Strong proficiency in agentic LLM Libraries/Technologies like LangChain, LangGraph, LangFuse, AutoGen, MCP, etc. * Familiarity with application development and deployment technologies like Databases ...

Senior Agentic AI Builder

San Jose, CA · On-site

$107K - $136K/yr

... LangGraph / LangChain - Hands-on experience building stateful, multi-step AI agent workflows with tool calling, checkpointing, and conditional routing • RAG - Practical experience implementing ...

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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 California?

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

What job categories do people searching Langgraph jobs in California look for?

The top searched job categories for Langgraph jobs in California are:

What cities in California are hiring for Langgraph jobs?

Cities in California with the most Langgraph job openings:

Infographic showing various Langgraph job openings in California 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.

Python Developer with Langchain and LangGraph

Sunnyvale, CA • On-site

PROLIM Global Corporation
IT Services • 201 - 500 employees

$60 - $82.50/hr

Full-time

Re-posted 18 days ago


Job description

Position: Python Developer with Langchain and LangGraph
Location: Sunnyvale, CA
Type: Contract
Job Description
Basic knowledge on Linux operations systems.
• Python (Professional)
• Docker and Kubenetes (Good to have)
• Experience in using and working with LLM's
• Langchain and LangGraph modules usage
• VectorDB's
• GitHub
• CICD pipelines
• Agentic AI
• MCP Protocols
• React JS (Good to have)
• Angular JS