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Langgraph Jobs (NOW HIRING)

RIT Solutions

Senior Software Engineer

Belmont, NC · Hybrid

$112.50K - $148.30K/yr

LangGraph Monitoring & Observability Tools: * Arize * Honeycomb * Splunk * OpenTelemetry (OTEL) Key Responsibilities * Design and develop scalable backend systems using TypeScript and Python * Build ...

RIT Solutions

Senior Software Engineer

Malvern, PA · Hybrid

$120.20K - $158.50K/yr

LangGraph Monitoring & Observability Tools: * Arize * Honeycomb * Splunk * OpenTelemetry (OTEL) Key Responsibilities * Design and develop scalable backend systems using TypeScript and Python * Build ...

Skywaves MP LLC

Python Developer

Manhattan, NY

$55.50 - $76.25/hr

We are seeking a Senior Python Backend-Heavy Full Stack AI Engineer with hands-on experience in LangGraph and agentic AI systems . This role focuses on building production-grade AI agents and ...

RIT Solutions

Senior Software Engineer

Atlanta, GA · Hybrid

$117.80K - $155.30K/yr

LangGraph Monitoring & Observability Tools: * Arize * Honeycomb * Splunk * OpenTelemetry (OTEL) Key Responsibilities * Design and develop scalable backend systems using TypeScript and Python * Build ...

Uplane

Founding AI Engineer

San Francisco, CA · On-site

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 ...

Uplane

Founding AI Engineer

San Francisco, CA · On-site

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 ...

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Langgraph information

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 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 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 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.

More about Langgraph jobs
What cities are hiring for Langgraph jobs? Cities with the most Langgraph job openings:
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Langgraph jobs near you
Infographic showing various Langgraph job openings in the United States as of May 2026, with employment types broken down into 94% Full Time, 1% Part Time, and 5% Contract. Highlights an 78% Physical, 5% Hybrid, and 17% Remote job distribution.

Agentic AI developer

Purple Drive Technologies

Pittsburgh, PA • On-site

Full-time

Posted 21 days ago

Job description

Overview:
Role: Agentic AI developer
Location: Pittsburgh, PA
Job Description
Skill: Agentic AI Developer
Must Have Technical/Functional Skills:
  • The Agentic AI Developer will design, build, and operationalize governed AI agent systems that autonomously plan, reason, and execute complex workflows across enterprise data and risk platforms.
  • This role focuses on developing multi agent, event driven AI solutions using Python, LangChain, LangGraph, and Azure AI Foundry, with strong emphasis on explainability, human in the loop control, and regulatory readiness.

Primary Skills:
  • Python, LangChain, LangGraph, Azure AI Foundry.

Roles & Responsibilities:
  • Agentic System Design: Architect, build, and optimize AI agents using multi-agent frameworks (e.g., LangGraph, AutoGen, CrewAI).
  • Reasoning & Planning: Implement advanced prompting strategies, such as chain-of-thought, reflection, and self-correction loops to enhance agent decision-making.
  • Tool Integration: Connect AI agents to APIs, databases, and third-party SaaS platforms to enable autonomous action-taking.
  • Memory & Context: Design short-term and long-term memory systems (RAG, vector databases) so agents can maintain state and context over long-running workflows.
  • Production Deployment: Transition prototypes from research to production-ready systems, ensuring low latency, high accuracy, and observability (e.g., using Amazon CloudWatch, LangSmith).
  • Safety & Governance: Implement AI guardrails, human-in-the-loop approval steps, and audit trails to ensure ethical and secure operation.

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