Schneider Electric - Principal Architect - AI Implementation

"Women Candidates Preferred"

Role Summary :

We are evolving our Product Lifecycle - Digital Thread architecture to become AI-enabled-supporting semantic search, knowledge retrieval (RAG), and agentic workflows that can assist engineers across requirements, change, traceability, and data quality.

This role will define the to be architecture and guide its implementation while grounding decisions in our current Orchestra/Aras footprint and integration landscape.

You will be responsible for AI architecture governance, AI-enablement strategy, and technical leadership across the platform ecosystem-ensuring value delivery, security/compliance, and operational sustainability.

To Be Architecture & Roadmap :

- Assess current PLM Digital Thread architecture (Aras Innovator platform + integrations with different enterprise systems) and define the target architecture for AI enablement.

- Establish a practical roadmap for GenAI + agentic capabilities that complements (not replaces) existing systems.

Agentic AI, MCP, and RAG Enablement :

- Design patterns for :

1. RAG (document/data ingestion, embeddings, vector stores, retrieval policies, evaluation/guardrails).

2. Agentic workflows (tool use, orchestration, multi-step reasoning, human-in-the-loop approvals, auditability) enabling orchestration among different platforms

3. MCP server/tooling concepts to expose Digital Thread capabilities safely to AI assistants (tool contracts, permissions, observability).

- Define governance: prompt/tool safety, data boundaries, authorization model, logging, red-teaming, and model risk controls.

Integration Architecture & Data Strategy :

- Drive integration patterns between Aras Innovator/Orchestra and tools like ALM/MBSE/PLM/DevOps systems (APIs, events, canonical models, data quality rules).

- Define a "single source of truth" strategy for key Digital Thread objects (requirements, change, configuration, test evidence, trace links).

Platform Engineering & Operations :

- Partner with engineering teams to implement architecture standards: CI/CD, environment strategy, secrets management, observability.

- Ensure performance, resiliency, and cost-conscious design for AI components (LLMOps, caching, batch vs. real-time, evaluation harnesses).

Stakeholder Alignment :

- Work with product owners, engineering leaders, compliance/legal/security to ensure solutions are usable, explainable, and policy-compliant.

- Influence vendor and partner engagements (Aras, integrators) with clear architecture guardrails and technical acceptance criteria.

Required Qualifications :

- Demonstrated experience delivering GenAI/RAG or AI-enabled enterprise solutions (not just PoCs): embeddings, retrieval, evaluation, guardrails, security.

- Strong integration architecture expertise: REST/event-driven design, identity/access management, data governance.

- Ability to translate business process needs into scalable technical architectures and guide implementation teams.

Preferred / Nice to Have :

- Familiarity with PLM/ALM/MBSE domains and Digital Thread concepts (traceability, configuration, baselines, change).

- TOGAF Certification would be an added plus

- Exposure to Aras Innovator architecture and customization patterns.

- Experience establishing AI governance frameworks (privacy, IP, retention, model risk, auditability).

- Experience with modern cloud/containers and MLOps/LLMOps toolchains.

Behavioral Competencies :

- Strong curiosity, experimentation mindset, and ability to make tradeoffs (value vs. complexity).

- Comfortable challenging the status quo respectfully; drives alignment across diverse stakeholders.

- Clear communicator: can explain complex architecture to executives and engineers.