PLM AI System Architect

siemens-energy

Gurgaon, India 7 Years Exp Posted 16d ago

Job Description

  • Strategic PLM Leadership: Drive AI adoption across the entire product lifecycle—from requirements and EBOM/MBOM management to digital twin enablement. 
  • Agentic AI Engineering: Design and orchestrate multi-agent systems capable of reasoning over complex PLM data, automating classification, and performing automated change impact analysis. 
  • Advanced Data Retrieval (RAG): Architect RAG pipelines that ensure engineering-grade accuracy, specifically tuned for structured PLM data and unstructured technical documentation. 
  • Architect and configure AI-powered retrieval systems that handle both structured PLM data (attributes, relations) and unstructured content (specifications, PDFs, drawings). 
  • AI Integration Architecture: Lead the development of MCP (Model Context Protocol) servers and secure integration layers to expose PLM services to enterprise AI agents. 
  • Governance & Scaling: Define the AI maturity roadmap, ensuring all deployments meet strict enterprise standards for data privacy, IP protection, and cost governance. 

What You Bring

  • Required total 7+ years of experience along with relevant experience 3 years in Artificial Intelligence.
  • PLM Domain Expertise: Basic understanding of PLM (Product Lifecycle Management). 
  • AI & Intelligent Systems: Proven expertise in Agentic AI frameworks, RAG architectures, and tool-calling patterns. Strong grasp of grounding strategies to mitigate hallucination risks in technical environments. 
  • Platform & Integration: Experience building AI integration layers (e.g., MCP servers), managing APIs/microservices, and deploying AI services in cloud-native environments. 
  • Communication: Ability to distill complex technical architecture into actionable business value narratives for senior leadership. 
  • Business Impact: Quantifiable reduction in cycle time and manual effort through AI automation. 
  • System Integrity: High accuracy and "trust scores" for AI-generated engineering outputs. 
  • Scalability: Successful deployment of reusable AI architectural patterns across the broader enterprise. 

Similar Openings for You