À propos de ce poste Staff/ Principa/ MTS Agentic AI Architect – Knowledge Engineering chez Micron Technology
Our vision is to transform how the world uses information to enrich life for all.
Micron Technology is a world leader in innovating memory and storage solutions that accelerate the transformation of information into intelligence, inspiring the world to learn, communicate and advance faster than ever.
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.
About Micron Technology, Inc.
We are an industry leader in innovative memory and storage solutions transforming how the world uses information to enrich life for all. With a relentless focus on our customers, technology leadership, and manufacturing and operational excellence, Micron delivers a rich portfolio of high-performance DRAM, NAND, and NOR memory and storage products through our Micron® and Crucial® brands. Every day, the innovations that our people create fuel the data economy, enabling advances in artificial intelligence and 5G applications that unleash opportunities — from the data center to the intelligent edge and across the client and mobile user experience.
To learn more, please visit micron.com/careers
All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, veteran or disability status.
To request assistance with the application process and/or for reasonable accommodations, please contact [email protected]
Micron Prohibits the use of child labor and complies with all applicable laws, rules, regulations, and other international and industry labor standards.
Micron does not charge candidates any recruitment fees or unlawfully collect any other payment from candidates as consideration for their employment with Micron.
AI alert: Candidates are encouraged to use AI tools to enhance their resume and/or application materials. However, all information provided must be accurate and reflect the candidate's true skills and experiences. Misuse of AI to fabricate or misrepresent qualifications will result in immediate disqualification.
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