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Langgraph Jobs in Kentucky (NOW HIRING)

LangChain, LangGraph, AutoGen, CrewAI) to support reasoning and decision-making in healthcare. * Enterprise Integration & Business Collaboration Embed AI into Humana's systems and workflows. Partner ...

LangChain, LangGraph, AutoGen, CrewAI) to support reasoning and decision-making in healthcare. * Enterprise Integration & Business Collaboration Embed AI into Humana's systems and workflows. Partner ...

LangChain, LangGraph, AutoGen, CrewAI) to support reasoning and decision-making in healthcare. * Enterprise Integration & Business Collaboration Embed AI into Humana's systems and workflows. Partner ...

LangChain, LangGraph, AutoGen, CrewAI) to support reasoning and decision-making in healthcare. * Enterprise Integration & Business Collaboration Embed AI into Humana's systems and workflows. Partner ...

LangChain, LangGraph, AutoGen, CrewAI) to support reasoning and decision-making in healthcare. * Enterprise Integration & Business Collaboration Embed AI into Humana's systems and workflows. Partner ...

LangChain, LangGraph, AutoGen, CrewAI) to support reasoning and decision-making in healthcare. * Enterprise Integration & Business Collaboration Embed AI into Humana's systems and workflows. Partner ...

LangChain, LangGraph, AutoGen, CrewAI) to support reasoning and decision-making in healthcare. * Enterprise Integration & Business Collaboration Embed AI into Humana's systems and workflows. Partner ...

LangChain, LangGraph, AutoGen, CrewAI) to support reasoning and decision-making in healthcare. * Enterprise Integration & Business Collaboration Embed AI into Humana's systems and workflows. Partner ...

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 Kentucky? For Langgraph jobs in Kentucky, the most frequently searched job titles are:
What cities in Kentucky are hiring for Langgraph jobs? Cities in Kentucky with the most Langgraph job openings:
Infographic showing various Langgraph job openings in Kentucky as of August 2026, with employment types broken down into 67% Full Time, and 33% Contract. Highlights an 82% In-person, and 18% Remote job distribution.

Full-time

Medical, Dental, Vision, Life, Retirement, PTO

Posted 19 days ago


Cognizant rating

7.4

Company rating: 7.4 out of 10

Based on 85 frontline employees who took The Breakroom Quiz

52nd of 72 rated business consultants


Job description

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About the Role
As an Agentic AI Engineer, you will make an impact by designing, developing, and deploying advanced AI agents and agentic systems that leverage Large Language Models (LLMs) to solve complex business challenges. You will be a valued member of our AI Engineering team and work collaboratively with data scientists, machine learning engineers, product managers, architects, and business stakeholders to deliver innovative AI-driven solutions.
Candidate must be legally authorized to work in the United States without the need for employer sponsorship, now or at any time in the future
In This Role, You Will:
  • Design and develop autonomous AI agents capable of reasoning, planning, and executing complex multi-step tasks using leading LLM technologies.
  • Build and orchestrate agentic workflows and multi-agent systems using LangGraph, enabling stateful execution, memory management, and agent collaboration.
  • Develop Retrieval-Augmented Generation (RAG), tool-calling, and function-calling solutions using LangChain and related frameworks.
  • Architect and integrate AI agents with enterprise systems, APIs, databases, and vector stores such as Pinecone, Chroma, Weaviate, and FAISS.
  • Implement memory frameworks including short-term, long-term, semantic, and episodic memory for intelligent agent behavior.
  • Design prompt engineering strategies and optimize LLM performance for accuracy, reliability, scalability, and cost efficiency.
  • Develop guardrails, validation layers, and human-in-the-loop workflows to ensure safe and reliable AI solutions.
  • Create and maintain evaluation frameworks to assess agent effectiveness, hallucination rates, and task completion metrics.
  • Deploy AI applications to production environments using AWS, Azure, or GCP, leveraging Docker, Kubernetes, and CI/CD pipelines.
  • Monitor, troubleshoot, and optimize production AI systems for performance, latency, scalability, and token utilization.
  • Collaborate with cross-functional teams to translate business requirements into innovative AI-powered solutions.
  • Stay current with emerging developments in agentic AI, LLMs, frameworks, and industry best practices.
Work Model
This is an onsite position based in Louisville, Kentucky, requiring attendance at the client or Cognizant office 5 days per week. Candidates should be comfortable working in a collaborative, office-based environment and partnering closely with cross-functional teams and stakeholders.
The working arrangements for this role are accurate as of the date of posting. This may change based on the project you're engaged in, as well as business and client requirements. Rest assured; we will always be clear about role expectations.
What You Need to Have to Be Considered
  • 8+ years of experience in software engineering, AI engineering, machine learning, or related technology roles.
  • Strong experience building solutions using Large Language Models (LLMs), Generative AI, and Agentic AI frameworks.
  • Hands-on expertise with LangChain and LangGraph for developing agentic workflows and multi-agent solutions.
  • Strong programming experience in Python and modern software development practices.
  • Experience designing and implementing Retrieval-Augmented Generation (RAG) architectures.
  • Experience integrating AI solutions with APIs, databases, enterprise applications, and vector databases.
  • Experience deploying applications in cloud environments such as AWS, Azure, or GCP.
  • Familiarity with Docker, Kubernetes, CI/CD pipelines, and production-grade application deployment.
  • Strong analytical, problem-solving, and collaboration skills.
These Will Help You Stand Out
  • Experience with multi-agent architectures and agent-to-agent communication frameworks.
  • Experience implementing memory management strategies for AI agents.
  • Knowledge of Responsible AI, AI governance, and AI safety best practices.
  • Experience evaluating and optimizing LLM outputs, token consumption, latency, and overall cost.
  • Familiarity with MLOps and AI application monitoring frameworks.
  • Experience working with open-source LLMs and emerging agentic AI technologies.
Salary and Other Compensation
The annual salary for this position is between depends on experience and other qualifications of the successful candidate.
This position is also eligible for Cognizant's discretionary annual incentive program, based on performance and subject to the terms of Cognizant's applicable plans.
Benefits
Cognizant offers the following benefits for this position, subject to applicable eligibility requirements:
  • Medical/Dental/Vision/Life Insurance
  • Paid holidays plus Paid Time Off
  • 401(k) plan and contributions
  • Long-term/Short-term Disability
  • Paid Parental Leave
  • Employee Stock Purchase Plan
Disclaimer
The salary, other compensation, and benefits information is accurate as of the date of this posting. Cognizant reserves the right to modify this information at any time, subject to applicable law.
About Cognizant:
Cognizant (Nasdaq: CTSH) is an AI Builder and technology services provider, bridging the gap between AI investment and enterprise value by building full-stack AI solutions for our clients. Our deep industry, process and engineering expertise enables us to build an organization's unique context into technology systems that amplify human potential, drive tangible outcomes and keep global enterprises ahead in a fast-changing world. See how at cognizant.ai or @cognizant.
Additional employment information
Compensation information is accurate as of the date of this posting. Cognizant reserves the right to modify this information at any time, subject to applicable law.
Applicants may be required to attend interviews in person or by video conference. In addition, candidates may be required to present their current state or government issued ID during each interview.
Cognizant is an equal opportunity employer. Your application and candidacy will not be considered based on race, color, sex, religion, creed, sexual orientation, gender identity, national origin, disability, genetic information, pregnancy, veteran status or any other characteristic protected by federal, state or local laws.
If you have a disability that requires reasonable accommodation to search for a job opening or submit an application, please email [email protected] for roles based in the Americas or [email protected] for roles based in India.

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