Generative AI Architect (Knowledge Graphs)

HMG America LLC is the best Business Solutions focused Information Technology Company with IT consulting and services, software and web development, staff augmentation and other professional services. One of our direct clients is looking for Generative AI Architect (Knowledge Graphs) in Seattle, WA. Below is the detailed job description.

Title: Generative AI Architect (Knowledge Graphs)

Location: Seattle, WA, US

Department: AI & Data Engineering

Employment Type: Full-time

Job Description:

We are seeking a Generative AI Architect to lead the design and implementation of cuttingedge AI solutions that harness the power of Large Language Models (LLMs), RetrievalAugmented 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.

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.

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.

Required Skills & Experience

Technical Expertise

10+ years in software or systems architecture, with significant experience in cloudnative, scalable AI solutions.

Hands-on experience with Graph Database technologies (e.g., Neo4j, Neptune,

Stardog, TigerGraph) and graph query languages such as Cypher, SPARQL, or

Gremlin.

Deep knowledge of ontology modeling, semantic web standards (OWL, RDF,

SKOS), and tools like Prot g or TopBraid.

Proven experience in building GenAI systems that leverage graph-based reasoning

and structured ontologies for retrieval, context, and disambiguation.

Advanced proficiency in cloud infrastructure services, including compute,

storage, identity, and networking across AWS, Azure, or Google Cloud Platform.

Strong expertise in containerization and orchestration (Kubernetes, Fargate) and

DevOps practices.

Familiarity with GenAI libraries such as LangGraph, CrewAI, AutoGen, and LLMOps

workflows.

Leadership Capabilities

Experience leading teams to implement enterprise-grade knowledge graph and

GenAI architectures.

Ability to translate complex, abstract business goals into structured, knowledgepowered GenAI solutions.

Skilled in client engagement and managing cross-functional teams through

ambiguity and rapid iteration.

Soft Skills

Clear and concise communication across technical and executive teams.

Strong presentation skills for delivering architecture walkthroughs and business

impact stories.

Proven ability to create reusable playbooks, templates, and solution guides.

Preferred Qualifications

Bachelor's or Master's degree in Computer Science, AI/ML, Semantic Web

Technologies, or related field.

Certifications in cloud architecture (AWS, Azure, Google Cloud Platform) and/or ontology engineering.

Published whitepapers, blog posts, or open-source contributions in GenAI,

GraphDBs, or knowledge representation are a plus.