Job Description Summary

The Principal Architect – AI & Equipment Data Management is responsible for defining and governing the enterprise architecture for AI-powered equipment intelligence platforms. The role focuses on enabling the Equipment Intelligence 2.0 transformation through scalable data platforms, fine-tuned LLMs, RAG-based intelligence layers, and agentic AI capabilities across connected equipment ecosystems.
The role provides architectural leadership across cloud, AI, and data platforms while ensuring alignment with enterprise security, privacy, compliance, and AI governance standards

Job Description

Key Responsibilities

1. Enterprise AI & Equipment Data Architecture

• Define and own the end-to-end architecture for equipment data platforms across device, cloud, data, and AI layers.

• Establish architectural standards, reusable patterns, and governance models for scalability, reliability, security, and compliance.

• Design and guide real-time and batch data ingestion, streaming, storage, and analytics architectures.

• Partner with product, engineering, and business leaders to align architecture with strategic business outcomes.

• Evaluate emerging AI, IoT, and cloud technologies to influence long-term platform strategy.

2. Equipment Intelligence 2.0 Transformation

• Lead the architecture strategy for Equipment Intelligence 2.0, including:

  • Fine-tuned LLM capabilities

  • Retrieval-Augmented Generation (RAG) intelligence layers

  • Agentic AI and workflow orchestration layers

  • AI-driven reasoning and decision-support systems

• Define scalable reference architectures and reusable frameworks for GenAI-enabled equipment intelligence solutions.

• Guide architecture decisions for AI platforms, vector databases, orchestration frameworks, model integration, and inference patterns.

• Ensure architecture designs align with Responsible AI, privacy, cybersecurity, and enterprise governance standards.

• Drive architecture reviews and secure approvals from enterprise Privacy, Security, and AI Technology Councils.

• Define technical standards and governance for enterprise adoption of LLM and agentic AI solutions.

• Development of a roadmap, design pattern, and implementation based upon a current vs. future state in a cohesive architecture viewpoint.

• Coordinate Technical Design Reviews (Concept, Development, and Evaluation TDR’s).

3. AI Solution Enablement & Delivery Guidance

• Guide engineering and ML teams in implementing AI/ML use cases such as predictive maintenance, anomaly detection, optimization, and automation.

• Provide architectural guidance for model deployment, integration patterns, evaluation strategies, and operational scalability.

• Collaborate with engineering teams to operationalize AI solutions within enterprise platforms.

• Ensure solutions are maintainable, scalable, and aligned with business and operational requirements.

• Support architecture reviews, technical decision-making, and solution planning across delivery programs.

4. Strategic Leadership & Stakeholder Management

• Partner with business, product, and technology leaders to define AI and data roadmaps.

• Translate complex technical concepts into clear recommendations for executive stakeholders.

• Provide architectural governance and technical leadership across multiple programs and teams.

• Influence enterprise technology strategy and drive adoption of modern AI and data capabilities.

5. Technology Modernization & Continuous Improvement

• Identify opportunities for platform modernization, architecture refactoring, and optimization.

• Improve key architecture KPIs including scalability, throughput, cost efficiency, and delivery cycle time.

• Guide engineering teams to implement improvements safely and effectively.

• Evaluate trade-offs and influence strategic technology decisions through >

  Additional Information

  Relocation Assistance Provided: No