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Langgraph Jobs in Colorado (NOW HIRING)

Experience with LangGraph and LangSmith Deployments is preferred. * Experience with cloud-based data lake and distributed data processing environments. Responsibilities * Design and manage AWS ...

Experience with LangGraph and LangSmith Deployments is preferred. * Experience with cloud-based data lake and distributed data processing environments. Responsibilities * Design and manage AWS ...

Experience with LangGraph and LangSmith Deployments is preferred. * Experience with cloud-based data lake and distributed data processing environments. Responsibilities * Design and manage AWS ...

Experience with LangGraph and LangSmith Deployments is preferred. * Experience with cloud-based data lake and distributed data processing environments. Responsibilities * Design and manage AWS ...

Experience with LangGraph and LangSmith Deployments is preferred. * Experience with cloud-based data lake and distributed data processing environments. Responsibilities * Design and manage AWS ...

Experience with LangGraph and LangSmith Deployments is preferred. * Experience with cloud-based data lake and distributed data processing environments. Responsibilities * Design and manage AWS ...

Experience with LangGraph and LangSmith Deployments is preferred. * Experience with cloud-based data lake and distributed data processing environments. Responsibilities * Design and manage AWS ...

Experience with LangGraph and LangSmith Deployments is preferred. * Experience with cloud-based data lake and distributed data processing environments. Responsibilities * Design and manage AWS ...

Experience with LangGraph and LangSmith Deployments is preferred. * Experience with cloud-based data lake and distributed data processing environments. Responsibilities * Design and manage AWS ...

Experience with LangGraph and LangSmith Deployments is preferred. * Experience with cloud-based data lake and distributed data processing environments. Responsibilities * Design and manage AWS ...

Experience with LangGraph and LangSmith Deployments is preferred. * Experience with cloud-based data lake and distributed data processing environments. Responsibilities * Design and manage AWS ...

Experience with LangGraph and LangSmith Deployments is preferred. * Experience with cloud-based data lake and distributed data processing environments. Responsibilities * Design and manage AWS ...

Experience with LangGraph and LangSmith Deployments is preferred. * Experience with cloud-based data lake and distributed data processing environments. Responsibilities * Design and manage AWS ...

Experience with LangGraph and LangSmith Deployments is preferred. * Experience with cloud-based data lake and distributed data processing environments. Responsibilities * Design and manage AWS ...

Experience with LangGraph and LangSmith Deployments is preferred. * Experience with cloud-based data lake and distributed data processing environments. Responsibilities * Design and manage AWS ...

Experience with LangGraph and LangSmith Deployments is preferred. * Experience with cloud-based data lake and distributed data processing environments. Responsibilities * Design and manage AWS ...

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 are popular job titles related to Langgraph jobs in Colorado?

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

What cities in Colorado are hiring for Langgraph jobs?

Cities in Colorado with the most Langgraph job openings:

Infographic showing various Langgraph job openings in Colorado as of August 2026, with employment types broken down into 1% Internship, 90% Full Time, 3% Part Time, 1% Temporary, and 5% Contract. Highlights an 77% Physical, 6% Hybrid, and 17% Remote job distribution.

AWS Platform Engineer

2T Consulting

Greenwood Village, CO • On-site

Full-time

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


Job description

We are seeking an experienced AWS Data Platform & AI Infrastructure Engineer with 5–8 years of experience in AWS infrastructure, data platforms, DevOps, and AI workloads.

Must-Have Skills

  • Strong experience with AWS S3, Glue, Athena, and EMR.
  • Hands-on experience with Terraform and AWS Infrastructure as Code.
  • Strong knowledge of VPC, IAM, Security Groups, VPC Peering, PrivateLink, and Transit Gateway.
  • Experience with Docker, GitLab CI/CD, and Artifactory.
  • Experience supporting Scala/Spark ETL pipelines.
  • Proficiency in Python, Linux, and Shell scripting.
  • Experience with CloudWatch, Prometheus, or Grafana.
  • Experience with REST APIs and AWS SDK/boto3.
  • Strong understanding of CI/CD, Git workflows, automated testing, and software engineering practices.
  • Experience with event-driven systems such as Kafka, SNS/SQS, or EventBridge.

AI / Data Platform Experience

  • Experience supporting AI agent runtime environments.
  • Experience with LangGraph and LangSmith Deployments is preferred.
  • Experience with cloud-based data lake and distributed data processing environments.

Responsibilities

  • Design and manage AWS infrastructure for Data Lake and AI workloads.
  • Manage cross-account connectivity, networking, IAM, and security.
  • Build and maintain CI/CD pipelines for AI agent deployments.
  • Support and troubleshoot Scala/Spark ETL pipelines and onboard new data sources.
  • Implement monitoring, alerting, automation, and incident response.
  • Develop reusable infrastructure and automation using Terraform and Python.
  • Collaborate with engineering teams and external platform owners on connectivity, security, and access requirements.