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Langgraph Jobs in Michigan (NOW HIRING)

... LangGraph, LangChain, and Model Context Protocol (MCP) for complex workflow orchestration, planning, and tool use Define patterns for ReAct, Chain-of-Thought, Tree-of-Thoughts, and agent-to-agent ...

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 ...

Transition from linear "chain" workflows to self-correcting agentic loops using frameworks like LangChain, LangGraph, or LlamaIndex to automate complex marketing analytics workflows. * Implement Tool ...

AI Engineer

Detroit, MI ยท On-site +1

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 ...

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 ...

Experience with Git and Azure DevOps * Experience with Agentic AI (LangGraph, MCP Servers, Agent Frameworks) * Experience with Data Handling (Pandas / NumPy, Apache Spark, Ray) Helpful Experience ...

Experience with Git and Azure DevOps * Experience with Agentic AI (LangGraph, MCP Servers, Agent Frameworks) * Experience with Data Handling (Pandas / NumPy, Apache Spark, Ray) Helpful Experience ...

Experience with Git and Azure DevOps * Experience with Agentic AI (LangGraph, MCP Servers, Agent Frameworks) * Experience with Data Handling (Pandas / NumPy, Apache Spark, Ray) Helpful Experience ...

Experience with Git and Azure DevOps * Experience with Agentic AI (LangGraph, MCP Servers, Agent Frameworks) * Experience with Data Handling (Pandas / NumPy, Apache Spark, Ray) Helpful Experience ...

Experience with Git and Azure DevOps * Experience with Agentic AI (LangGraph, MCP Servers, Agent Frameworks) * Experience with Data Handling (Pandas / NumPy, Apache Spark, Ray) Helpful Experience ...

Experience with Git and Azure DevOps * Experience with Agentic AI (LangGraph, MCP Servers, Agent Frameworks) * Experience with Data Handling (Pandas / NumPy, Apache Spark, Ray) Helpful Experience ...

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Langgraph information

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 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 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 popular job titles related to Langgraph jobs in Michigan? For Langgraph jobs in Michigan, the most frequently searched job titles are:
What cities in Michigan are hiring for Langgraph jobs? Cities in Michigan with the most Langgraph job openings:
Infographic showing various Langgraph job openings in Michigan as of July 2026, with employment types broken down into 92% Full Time, 4% Part Time, and 4% Contract. Highlights an 77% Physical, 5% Hybrid, and 18% Remote job distribution.
Other Service Line - Business Development - GENBDR

Other Service Line - Business Development - GENBDR

Ztek Consulting INC

Detroit, MI โ€ข On-site

Contractor

Posted 29 days ago


Job description

Job Title: Agentic AI engineer
Location:- Detroit 48226 or Charlotte 28202 (3 Days Hybrid)
Job Responsibilities:
Design, develop, and support cloud-native automation and Al agent workflows using Python and LLM orchestration frameworks (LangChain / LangGraph), deployed on AWS using containerized architectures.
โ€ข Develop automation solutions using Python.
โ€ข Build Al agents using LangChain and LangGraph to orchestrate tools, APls, and workflows.
โ€ข Integrate automations with enterprise systems via REST APIs and databases.
โ€ข Containerize services using Docker and support CI/CD pipelines.
โ€ข Deploy and operate solutions on AWS (IAM, S3, Lambda, ECS/Fargate, CloudWatch).
โ€ข Reasoning Engines: Experience with frontier models like GPT-4o, Claude 3.5, and Llama 3.x/4 specifically for tool-calling and JSON-mode outputs Azure OpenAI proficiency .
โ€ข Agentic RAG 2.0: Develop "iterative retrieval" systems where agents autonomously decide if they have enough information or if they need to perform additional searches/queries.
โ€ข Implement logging, error handling, and basic monitoring.
โ€ข Collaborate with onshore architects and follow defined architecture standards.
Must Have skills:
Python (automation, backend services).
โ€ข JavaScript (React/Node JS)
โ€ข LangChain and/or LangGraph hands-on experience.
โ€ข Docker and container-based deployments.
โ€ข AWS Cloud Practitioner-level knowledge with hands-on exposure.
โ€ข REST APIS, JSON, Git.