Sr LangGraph Developer

Sr. LangGraph Developer

Location: Dallas, TX (Primary) / Charlotte, NC (Secondary) - Hybrid, 3 days per week onsite



Employment Type: Contract - W2 only (No C2C)

Interview Process:

• Round 1 (Video, 30 minutes) Initial screening against requirement fit.
• Round 2 (In-person, Dallas or Charlotte office) Theoretical discussion plus a coding challenge covering tool calling and intent routing. No AI assistance permitted.
• Final Round (In-person, Dallas or Charlotte office) Technical and coding evaluation.

Note: Rounds 2 and 3 are conducted in person. Coding must be completed without AI assistance - this is a graded part of the evaluation.

Job Summary:

We are looking for a highly experienced and technically elite Senior LangGraph Developer to architect and implement cutting-edge agentic workflows using LangGraph. This role demands deep expertise in building stateful, multi-agent systems that leverage LLMs for complex decision-making, orchestration, and automation. You will be responsible for designing resilient, scalable LangGraph architectures that power intelligent applications across domains.

Key Responsibilities:

• Design and implement advanced LangGraph workflows with complex node logic, branching, and memory management.
• Lead the development of agentic systems that interact, reason, and adapt dynamically.
• Optimize performance and scalability of LangGraph graphs in production environments.
• Integrate LangGraph with external APIs, databases, and LLMs to create seamless, intelligent pipelines.
• Mentor junior developers and establish best practices for LangGraph development.
• Collaborate with AI researchers and product teams to translate abstract ideas into robust LangGraph implementations.
• Design, build, and deploy production-grade agentic AI systems.

Required Skills & Experience:

• 10+ years in software engineering, with at least 2+ years in LangGraph or comparable agentic frameworks.
• Expert-level proficiency in Python, asynchronous programming, and graph-based computation.
• Deep understanding of LLM orchestration, memory management, and stateful agent design.
• Experience with LangChain, OpenAI, Anthropic, or similar LLM platforms.
• Strong grasp of workflow debugging, graph visualization, and LangGraph Studio.
• Proven ability to build production-grade agentic systems with high reliability and fault tolerance.
• Strong command of GenAI fundamentals, prompt engineering, and agentic system design.
• Experience with monitoring and CI/CD processes for AI systems.
• Familiarity with evaluation metrics across RAG, batch processing, and agentic pipelines.