What are the responsibilities and job description for the Generative AI Architect :: Seattle, WA (Onsite) :: Fulltime/Contract position at TESTINGXPERTS, INC. DBA DAMCOSOFT?
Job Details
Job Summary: We are seeking a Generative AI Architect to lead the design and implementation of cutting-edge AI solutions that harness the power of Large Language Models (LLMs), Retrieval Augmented Generation (RAG), agentic architectures, and Knowledge Graphs. This role demands a visionary technologist with deep expertise in graph-based data modeling, ontology design, and cloud-native deployment, capable of building end-to-end AI ecosystems that drive business value.
Key Responsibilities
Technical Architecture & Implementation
Design, build, and scale large, production-grade Generative AI systems on cloud infrastructure (AWS, Azure, Google Cloud Platform).
Architect and implement Graph Databases (e.g., Neo4j, Amazon Neptune, TigerGraph, Stardog) and knowledge graph pipelines to power context-aware GenAI systems.
Build and extend knowledge bases and ontologies using OWL (Web Ontology Language) and tools such as Prot g , TopBraid Composer, Stardog Studio, and KGForge.
Integrate ontologies into GenAI pipelines to enable semantic reasoning, concept disambiguation, and domain-aware responses.
Develop and optimize ingestion pipelines for structured and unstructured data into graph structures using RDF, SPARQL, and JSON-LD formats.
Architect RAG pipelines using LLMs (OpenAI, Anthropic, Mistral, etc.) in combination with vector stores (Pinecone, FAISS, Weaviate) and graph-based retrieval systems for enhanced contextual search.
Deploy and maintain GenAI applications using frameworks like LangGraph, CrewAI, and AutoGen, focusing on agent orchestration and multi-agent collaboration.
Design cloud-native, containerized RESTful services (Kubernetes, ECS/Fargate, Azure Container Apps) integrated with scalable GenAI APIs.
Technical Architecture & Implementation
Design, build, and scale large, production-grade Generative AI systems on cloud infrastructure (AWS, Azure, Google Cloud Platform).
Architect and implement Graph Databases (e.g., Neo4j, Amazon Neptune, TigerGraph, Stardog) and knowledge graph pipelines to power context-aware GenAI systems.
Build and extend knowledge bases and ontologies using OWL (Web Ontology Language) and tools such as Prot g , TopBraid Composer, Stardog Studio, and KGForge.
Integrate ontologies into GenAI pipelines to enable semantic reasoning, concept disambiguation, and domain-aware responses.
Develop and optimize ingestion pipelines for structured and unstructured data into graph structures using RDF, SPARQL, and JSON-LD formats.
Architect RAG pipelines using LLMs (OpenAI, Anthropic, Mistral, etc.) in combination with vector stores (Pinecone, FAISS, Weaviate) and graph-based retrieval systems for enhanced contextual search.
Deploy and maintain GenAI applications using frameworks like LangGraph, CrewAI, and AutoGen, focusing on agent orchestration and multi-agent collaboration.
Design cloud-native, containerized RESTful services (Kubernetes, ECS/Fargate, Azure Container Apps) integrated with scalable GenAI APIs.
Leadership & Stakeholder Management
Lead multi-disciplinary teams to deliver end-to-end GenAI Knowledge Graph solutions.
Define and drive execution plans that marry business objectives with ontology-driven GenAI capabilities.
Guide customers on best practices in graph modeling, ontology lifecycle management, and LLM-enhanced search and reasoning.
Collaborate with cloud architects to build reusable knowledge-enabled AI components within enterprise ecosystems.
Lead multi-disciplinary teams to deliver end-to-end GenAI Knowledge Graph solutions.
Define and drive execution plans that marry business objectives with ontology-driven GenAI capabilities.
Guide customers on best practices in graph modeling, ontology lifecycle management, and LLM-enhanced search and reasoning.
Collaborate with cloud architects to build reusable knowledge-enabled AI components within enterprise ecosystems.
Collaboration, Communication & Delivery
Develop technical documentation including knowledge graph blueprints, ontology design guides, and RAG architecture diagrams.
Present solution designs and demonstrations to both technical and non-technical stakeholders.
Utilize agile tools (Azure DevOps, JIRA) to manage development lifecycles, sprint planning, and milestone tracking.
Develop technical documentation including knowledge graph blueprints, ontology design guides, and RAG architecture diagrams.
Present solution designs and demonstrations to both technical and non-technical stakeholders.
Utilize agile tools (Azure DevOps, JIRA) to manage development lifecycles, sprint planning, and milestone tracking.
Employers have access to artificial intelligence language tools (“AI”) that help generate and enhance job descriptions and AI may have been used to create this description. The position description has been reviewed for accuracy and Dice believes it to correctly reflect the job opportunity.