Agentic AI Engineer - LangGraph

Company Description FutureStack AI builds intelligent AI systems that continuously learn and improve from real-world user feedback. The company offers three core products on a unified technology stack: Smart Agents for specialized business functions, AI Knowledge Bases to capture and operationalize institutional knowledge, and ContentOps Enterprise for human-AI powered content creation and distribution. All products share a common foundation of Drupal CMS, a modern React frontend, and a vector database for long-term memory, enabling organizations to start with one solution and scale seamlessly. FutureStack AI’s first vertical focus is real estate, with AI assistants deployed for South Florida agents through an early-access pilot and broader enterprise, cross-industry programs planned. The company is headquartered in Miami, FL, and registered in Delaware.Role Description As an Agentic AI Engineer - LangGraph at FutureStack AI, you will design, implement, and optimize agentic workflows powered by LangGraph and related tools. You will build multi-agent systems that integrate with our unified stack (Drupal CMS, React frontend, vector databases) to support Smart Agents, AI Knowledge Bases, and ContentOps Enterprise. Daily responsibilities include architecting agent graphs, implementing tool-calling and memory strategies, integrating external APIs and data sources, and ensuring robust, observable, and secure AI pipelines. You will collaborate with product, engineering, and domain experts to translate business requirements into reliable agent behaviors, conduct experiments, and iterate based on user feedback and performance metrics. This is a full-time, remote role that involves close coordination with a distributed team across time zones.Qualifications Agentic AI and orchestration: Experience with LangGraph or similar agent frameworks (e.g., LangChain, AutoGen, Swarm), multi-agent systems, tool-calling, and long-term memory architectures.Core software engineering: Proficiency in Python or TypeScript/JavaScript, software design patterns, testing, debugging, and version control (Git) in production environments.AI and ML foundations: Understanding of LLMs, prompt engineering, retrieval-augmented generation (RAG), embeddings, and vector databases (e.g., Pinecone, Weaviate, pgvector, Qdrant).Web and platform integration: Experience integrating AI services with web applications (REST/GraphQL APIs), working with modern frontends (React or similar) and CMS platforms (Drupal or comparable).Systems reliability and observability: Familiarity with monitoring, logging, tracing, and evaluation of agent behavior, including performance, cost, safety, and guardrail strategies.Collaboration and communication: Ability to work effectively in a remote, cross-functional team, communicate technical concepts clearly, and incorporate user and stakeholder feedback.Education and