AI / Full Stack Engineer (Dallas/Tampa)

Role OverviewWe are seeking an experienced AI / Full Stack Engineer to work on cutting-edge AI-driven applications leveraging Large Language Models (LLMs), graph databases, and cloud-native services. The ideal candidate will have strong expertise across both backend and frontend development, along with hands-on experience building intelligent applications using GCP and modern AI frameworks.Key ResponsibilitiesDesign, develop, and deploy full-stack applications using React, Node.js, and PythonBuild and integrate LLM-powered solutions using APIs such as Vertex AI, Gemini, OpenAI, or AnthropicImplement RAG (Retrieval-Augmented Generation) pipelines, including chunking, embedding, vector search, and re-rankingDevelop and optimize Graph RAG architectures, leveraging graph data to enhance LLM responsesWork with graph databases (Neo4j, Spanner Graph, or similar) for schema design and query developmentBuild and deploy applications on Google Cloud Platform (GCP) including Cloud Run, BigQuery, Pub/Sub, and GCSDesign and maintain Elasticsearch indices and implement search functionality using query DSLDevelop and manage agentic workflows using LangGraph, including multi-agent systems and human-in-the-loop patternsCollaborate with cross-functional teams to deliver scalable and production-ready AI solutionsMonitor and improve model performance using tools such as Galileo for evaluation and hallucination detectionRequired Skills4+ years of full stack development experience across frontend and backendStrong proficiency in React, Python, and Node.jsHands-on experience with GCP services (Vertex AI, Cloud Run, BigQuery, Pub/Sub, Load Balancer, GCS)Experience working with graph databases (Neo4j, Spanner Graph, or similar)Good understanding of Elasticsearch (indexing, mappings, query DSL)Strong knowledge of SQL and data processing (BigQuery / Cloud SQL)Familiarity with vector databases (Vertex AI Vector Search, Pinecone, pgvector, etc.)Solid understanding of RAG architectures and LLM integrationExperience consuming LLM APIs, including function calling and structured outputsHands-on experience with LangGraph for agent-based workflowsNice to HaveExperience with graph-based RAG frameworks (Spanner Graph, LlamaIndex Property Graph)Exposure to NLP pipelines and entity extraction tools (spaCy, GLiNER, etc.)Knowledge of graph algorithms (PageRank, shortest path, centrality)Background in ontology or knowledge graph designExperience with GCP data pipelinesFamiliarity with the LangChain ecosystemQualificationsBachelor’s degree in Computer Science or a related fieldStrong analytical and problem-solving skillsAbility to work in a fast-paced, collaborative environmentWhat We’re Looking ForHands-on engineer with strong AI + full stack development skillsPassionate about building next-generation intelligent systemsStrong ownership mindset with focus on delivery and scalability