Industrial AI Architect Consultant

Overview:

The Industrial AI Architect Consultant is responsible for defining, governing, and evolving the end-to-end architecture, technology strategy, and technical roadmap of Industrial AI platforms and solutions across multiple industrial domains. The role serves as a senior technical authority, ensuring Industrial AI capabilities are scalable, secure, interoperable, and aligned with business objectives, enterprise architecture principles, and emerging technology trends. The role establishes architecture frameworks, evaluates emerging technologies, defines standards and reference architectures, and enables the delivery of reusable and sustainable Industrial AI capabilities across diverse industrial environments.

Key Responsibilities:

• Define and maintain the target-state architecture, technology strategy, and technical roadmap for Industrial AI platforms and solution domains.

• Establish architecture principles, standards, and governance frameworks that promote scalability, interoperability, security, and resilience.

• Develop reference architectures, design patterns, and integration frameworks that enable reusable and production-ready Industrial AI solutions.

• Ensure alignment between business priorities, digital transformation initiatives, and Industrial AI platform capabilities.

• Review and assess solution architectures to ensure compliance with enterprise architecture standards, regulatory requirements, and technology policies.

• Evaluate emerging technologies, industry trends, and innovation opportunities to continuously enhance Industrial AI capabilities and technology competitiveness.

• Define data, AI, and integration standards that support consistent, secure, and scalable Industrial AI implementations.

• Provide architectural guidance and advisory support to engineering, product, and delivery teams throughout the solution lifecycle.

• Assess technology investments, platform modernization initiatives, and technology rationalization opportunities to improve performance, efficiency, and sustainability.

• Collaborate with technology partners, vendors, and ecosystem stakeholders to evaluate capabilities and accelerate innovation and solution delivery.

• Provide architectural oversight for Industrial AI capabilities supporting use cases such as Asset Integrity & Reliability, Digital Twins, Production Optimization, Smart Wells, Intelligent Substations, Autonomous Operations, Energy Optimization, Industrial Copilots, and Sustainability Intelligence.

• Produce architecture documentation, technical standards, and governance artifacts that support long-term platform evolution and knowledge sharing.

• Act as a trusted technical advisor and subject matter expert on Industrial AI architecture, enterprise integration, and emerging industrial technologies.

Qualifications:

Education:

• Bachelor's degree in Computer Science, Software Engineering, Information Systems, Electrical Engineering, Industrial Engineering, or a related discipline (Master is preferred).

• Professional certifications in Enterprise Architecture, Cloud Platforms, Artificial Intelligence, Data Architecture, or Industrial Digital Technologies are advantages.

Experience:

• 12–16 years of experience in enterprise architecture, digital platforms, industrial technologies, artificial intelligence, machine learning solutions, or related architecture roles.

• Proven experience designing and governing large-scale digital, AI, cloud, or industrial platforms in complex enterprise environments.

• Experience in industrial sectors such as energy, utilities, manufacturing, oil and gas, or infrastructure is highly preferred.

• Demonstrated experience in technology strategy, architecture governance, platform modernization, and enterprise-scale digital transformation initiatives.

• Hands-on experience defining reference architectures, integration frameworks, and technology standards for complex technology ecosystems.

Skills & Competencies:

• Strong understanding of Industrial AI, IIoT, Digital Twins, industrial data platforms, cloud technologies, and enterprise integration architectures

• Platform Modernization and Technology Rationalization

• Security, Scalability, and Resilience Engineering

• Technology Assessment and Innovation Evaluation

• Vendor and Technology Ecosystem Management