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Langgraph Jobs in Delaware (NOW HIRING)

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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 Delaware? For Langgraph jobs in Delaware, the most frequently searched job titles are:
What job categories do people searching Langgraph jobs in Delaware look for? The top searched job categories for Langgraph jobs in Delaware are:
What cities in Delaware are hiring for Langgraph jobs? Cities in Delaware with the most Langgraph job openings:
Langgraph jobs near you

Tech Lead / Lead Architect - RAG and Agentic AI

HAGNOS TECH LLC

Wilmington, DE • On-site

$53.50 - $73.50/hr

Other

Posted 7 days ago

New

Job description

Job Title: Tech Lead / Lead Architect – RAG & Agentic AI
Location: Columbus, OH/ Wilmington, DE – 3 days onsite role
Local candidate: locals only
Duration: Long Term Project
Interview Mode: Phone + video
Visa: H1B
 
Role Summary:
Lead architecture, design, and delivery of Agentic AI and RAG-based solutions, partnering with customers and internal teams to build scalable, secure, and high-impact AI systems.
Must-Have:
  1. Strong experience in RAG pipelines, embeddings, vector DBs, LLM orchestration, and prompting techniques.
  2. Hands-on expertise in AWS (Lambda, API Gateway, Bedrock, S3, OpenSearch, IAM, VPC, Secrets Manager).
  3. Ability to design end-to-end AI architecture and build PoCs before committing solutions to customers.
  4. Deep understanding of AI guardrails (toxicity, hallucination control), data privacy, and cloud security patterns.
  5. Proven ability to lead from the front, mentor teams, and own delivery under tight timelines and high visibility.
  6. Strong customer communication skills – ability to explain architecture, trade-offs, and risks clearly.
  7. Experience handling model evaluation, observability, performance tuning, and cost optimization in production AI systems.
  8. Expertise in API design, microservices integration, and event-driven architectures for AI systems.
Good-to-Have:
  1. Experience with Agentic AI frameworks (LangGraph, CrewAI, AutoGen, Semantic Kernel, etc.).
  2. Exposure to marketing domain use cases (campaign optimization, personalization, analytics, insights).
  3. Familiarity with multi-agent orchestration, tool usage (MCP), and human-in-loop workflows.

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