About the Role

Lead enterprise architecture and strategy for Agentic AI and Knowledge Engineering at scale, designing multi-agent systems, knowledge graphs, RAG/GraphRAG solutions, and hybrid cloud/on-prem AI platforms. Drive adoption of Claude and AWS AgentCore patterns, enable secure MCP-based tool integrations, and mentor teams to deliver knowledge-driven AI capabilities for engineering and manufacturing use cases.

Job Description

Role

Staff/Principal-level Agentic AI Architect focused on Knowledge Engineering. The role defines and drives enterprise architecture for Agentic AI, knowledge fabrics, and AI-powered decision systems across cloud (AWS, GCP) and on-prem environments, enabling agent runtimes, multi-agent collaboration, and knowledge-driven workflows.

Key Responsibilities

Define AI strategy and enterprise architecture for Agentic AI, knowledge engineering, and AI-powered decision systems.
Design scalable multi-agent architectures (A2A collaboration, memory systems, reasoning frameworks, workflow orchestration).
Architect agentic workflows leveraging the Claude ecosystem and AWS AgentCore runtime patterns.
Design MCP-based secure connectivity for agents to access enterprise tools, APIs, knowledge repositories, and data platforms.
Architect enterprise knowledge fabrics: ontologies, taxonomies, metadata models, and knowledge graphs for engineering/manufacturing use cases.
Design and optimize RAG and GraphRAG solutions, including retrieval, semantic search, grounding, citation, and graph traversal.
Develop LLM Wiki architecture, knowledge curation workflows, governance, and knowledge lifecycle processes.
Implement semantic integration: entity resolution, schema mapping, and cross-source knowledge integration across cloud and on-prem sources.
Build AI-powered reasoning: graph traversal, semantic reasoning, and context-aware agent capabilities.
Design hybrid platform architectures spanning AWS, GCP, on-prem compute, Kubernetes, distributed storage, and hybrid data platforms.
Establish AI governance: security, compliance, access control, observability, explainability, and Responsible AI practices.
Evaluate emerging technologies, define reference architectures, lead POCs, and drive platform adoption across engineering organizations.
Collaborate cross-functionally with engineering, manufacturing, product, validation, data, and business teams.

Requirements

Bachelor’s degree in Computer Science, AI, Data Science, Software Engineering, or related technical field.
8+ years of experience in software engineering, AI/ML, enterprise architecture, platform engineering, or knowledge engineering.
Experience designing and delivering enterprise-scale Agentic AI, Generative AI, and RAG/GraphRAG solutions.
Hands-on experience or strong working knowledge of the Claude ecosystem, agentic coding workflows, MCP integrations, and AWS AgentCore-style platforms.
Experience with hybrid architectures across AWS, GCP, on-premises systems, Kubernetes, and distributed enterprise platforms.
Proven technical leadership: architecture, technology selection, solution delivery, and organizational adoption.
Strong communication, stakeholder management, and cross-functional collaboration skills.

Preferred Experience

Domain experience applying AI and knowledge engineering in industrial/engineering contexts such as semiconductor, NAND, storage, firmware, validation, manufacturing, reliability, quality, or systems engineering.

Technical Highlights

Relevant technologies and tools called out include Claude/Claude Code workflows, AWS AgentCore, AWS, GCP (BigQuery), Kubernetes, Neo4j, AWS Neptune, RDF/OWL, Cypher, SPARQL, vector DBs (Pinecone, ChromaDB, Weaviate, Milvus, Qdrant, FAISS), Python, LangChain, LlamaIndex, LangGraph, and enterprise documentation systems (JIRA, Confluence, SharePoint, Bitbucket).

Notes

Role emphasizes leveraging AI-assisted (vibe) coding techniques and AI tools to automate workflows and improve engineering efficiency.

Staff/ Principa/ MTS Agentic AI Architect – Knowledge Engineering