Staff/ Principa/ MTS Agentic AI Architect – Knowledge Engineering

Responsibilities 

• AI Strategy & Architecture: Define and drive enterprise architecture for Agentic AI, Knowledge Engineering, and AI-powered decision systems across AWS, GCP, and on-prem environments. 

• Agentic AI Platforms: Design scalable multi-agent architectures using A2A collaboration, memory systems, reasoning frameworks, tool use, and workflow orchestration. 

• Claude & AWS AgentCore Enablement: Architect agentic workflows that leverage the Claude ecosystem, Claude Code-style engineering workflows, and AWS AgentCore-based agent runtime patterns. 

• MCP-Based Connectivity: Architect MCP-based access patterns that allow agents to securely interact with enterprise tools, APIs, knowledge repositories, data platforms, and engineering systems. 

• Knowledge Engineering: Architect enterprise knowledge fabrics, ontologies, taxonomies, metadata models, and knowledge graphs for engineering and manufacturing use cases. 

• RAG & GraphRAG Solutions: Design and optimize retrieval, semantic search, grounding, citation, graph traversal, and context engineering frameworks. 

• Knowledge Management: Develop LLM Wiki architecture, knowledge curation workflows, governance, and knowledge lifecycle processes. 

• Semantic Integration: Implement entity resolution, schema mapping, semantic interoperability, and cross-source knowledge integration across cloud and on-prem sources. 

• AI-Powered Reasoning: Build graph traversal, semantic reasoning, and context-aware agent capabilities across connected knowledge ecosystems. 

• Hybrid Platform Architecture: Design technology-agnostic AI solutions across AWS, GCP, on-premises compute, Kubernetes, distributed storage, and hybrid data platforms. 

• AI Governance: Establish standards for security, compliance, access control, observability, explainability, Responsible AI, and operational excellence. 

• Technology Leadership: Evaluate emerging technologies, define reference architectures, and drive AI platform adoption across engineering organizations. 

• multi-functional Collaboration: Partner with engineering, manufacturing, product, validation, data, and business teams to identify and deliver high-value AI solutions. 

• Innovation & Enablement: Lead proof-of-concepts, mentor technical teams, and promote standard processes in Agentic AI, Knowledge Engineering, and software architecture. 

Expertise 

• Claude Ecosystem: Claude, Claude Code-style coding workflows, prompt/context design, agentic engineering workflows, skill-based automation, MCP-enabled tool access, and enterprise adoption patterns. 

• AWS AgentCore & AWS AI Architecture: AWS AgentCore, AWS-native and hybrid agent runtime patterns, compute, storage, serverless, large-scale data processing, managed graph or retrieval services, and secure enterprise deployment patterns. 

• Agentic AI & A2A Systems: A2A-based agent collaboration, ReAct, Plan-and-Execute, Reflection, Supervisor Patterns, Tool Use, Memory Systems, and Workflow Orchestration. 

• MCP & Tool Connectivity: MCP-based integration with enterprise tools, APIs, data sources, knowledge repositories, agent tools, and governed execution environments. 

• Generative AI & Retrieval: Large Language Models, RAG, GraphRAG, Semantic Search, Retrieval Optimization, Reranking, Grounding, and Context Engineering. 

• Knowledge Graphs & Semantic Systems: Ontology Engineering, Taxonomy Design, Semantic Modeling, Knowledge Representation, and Enterprise Knowledge Architecture. 

• Graph Technologies: Neo4j, AWS Neptune, RDF/OWL, Property Graphs, Graph Traversal, Graph Reasoning, Cypher, and SPARQL. 

• Vector Databases: Pinecone, ChromaDB, Weaviate, Milvus, Qdrant, FAISS, and similar retrieval platforms. 

• AI Development Frameworks: Python, LangChain, LlamaIndex, LangGraph, Claude Code-compatible workflows, and AI Orchestration Frameworks. 

• Document Intelligence & Knowledge Ingestion: JIRA, Confluence, SharePoint, Bitbucket, Wikis, Specifications, Technical Documents, and Enterprise Knowledge Repositories. 

• Embedding & Retrieval Pipelines: Embedding Models, Metadata Extraction, Vectorization, Indexing, Document Processing, and Retrieval Evaluation. 

• Entity Resolution & Semantic Integration: Schema Mapping, Master Data Alignment, Semantic Interoperability, and Cross-Source Knowledge Integration. 

• GCP Architecture: GCP-native and hybrid AI patterns, including BigQuery-centered analytics, data pipelines, feature engineering, and manufacturing data integration. 

• On-Premises Engineering Systems: Integration with local engineering repositories, validation environments, tester data, file systems, sensitive IP stores, and governed internal platforms. 

• Hybrid Enterprise Integration: APIs, Microservices, Event-Driven Architectures, Enterprise Integration Patterns, Observability, Security, Governance, and Policy Enforcement. 

• Proven ability to leverage AI‑assisted (vibe) coding techniques to improve efficiency or automate design and analysis methodologies

• Leverage AI tools to automate the tools and workflow

  Applying Artificial Intelligence in workflows to improve build efficiency

Qualifications 

• Education: Bachelor's degree in Computer Science, Artificial Intelligence, Data Science, Software Engineering, or a related technical field. 

• Experience: 8+ years in software engineering, AI/ML, enterprise architecture, platform engineering, or knowledge engineering. 

• AI & Knowledge Systems: Experience designing and delivering enterprise-scale Agentic AI, Generative AI, RAG/GraphRAG, and knowledge-driven solutions. 

• Hybrid Architecture: Experience designing solutions across AWS, GCP, on-premises systems, Kubernetes, and distributed enterprise platforms. 

• Claude / Agentic Tooling: Hands-on experience or strong working knowledge of the Claude ecosystem, agentic coding workflows, MCP-based integrations, and AWS AgentCore-style agent platforms. 

• Technical Leadership: Proven ability to lead architecture, technology selection, solution delivery, and organizational adoption of emerging technologies. 

• Communication & Collaboration: Strong stakeholder management, communication, problem-solving, and cross-functional leadership skills. 

Preferred Domain Exposure 

• Industrial / Engineering Context: Experience applying AI and knowledge engineering to semiconductor, NAND, storage, firmware, validation, manufacturing, reliability, quality, product lifecycle, root cause analysis, or systems engineering environments.