Sr. Engineering AI Enterprise Architect

At TE, you will unleash your potential working with people from diverse backgrounds and industries to create a safer, sustainable and more connected world.

The Engineering AI Enterprise Architect is a senior AI technical authority responsible for the safe, consistent and scalable use of AI across a global engineering organisation. The role owns the Engineering AI solution design framework, including the architecture principles, reusable patterns, technical standards and production expectations used by business units to design AI models, agents, workflows and solution concepts. It helps identify common AI capabilities, reduce duplication and ensure local solutions are positioned for broader enterprise scale.

Working within a wider delivery model, the role provides AI expertise across solution design, model and agent quality, architecture fit, integration quality and technical issue resolution. It requires deep AI solution design expertise, credibility in engineering environments, enterprise architecture awareness and the ability to guide technical contributors without relying on direct authority. The role partners closely with Engineering Systems and Data. The AI Enterprise Architect is accountable for the AI technical framework, solution patterns, model and agent design standards and long-term scalability decisions. The Engineering Systems and Data team are accountable for the systems, data, supplier and integration capabilities required to deliver those AI patterns effectively.

Your main tasks:

• Define and maintain the engineering AI technical framework, including architecture principles, reusable patterns, readiness criteria, governance checkpoints and lifecycle expectations.
• Scale AI solutions across business units by assessing local designs, identifying reuse potential, industrialising successful prototypes and guiding solutions toward production ready capability.
• Create and maintain the engineering AI technology roadmap across tools, agents, models, workflows, data products, integrations and reusable services.
• Identify common AI capabilities required across multiple use cases, such as retrieval, similarity search, recommendation logic, engineering reasoning, orchestration, evaluation and shared data services.
• Coordinate architecture alignment across business units, engineering teams, systems leads, subject matter experts, IT, data teams, suppliers and vendors.
• Review technical proposals and solution designs for scalability, maintainability, security, integration quality, reuse potential and enterprise architecture fit.
• Embed security, data, IP protection, responsible AI, testing, documentation, monitoring and support expectations into practical delivery standards.
• Reduce duplicate development by steering teams toward shared platforms, common services and standard architecture patterns.

Your ideal profile:

• Proven track record designing and scaling AI-enabled solutions across engineering or other complex operational environments.
• Experience acting as a senior AI technical authority, including defining standards, reviewing solution designs and driving reuse across multiple teams.
• Practical experience taking GenAI, agentic AI, RAG, intelligent automation or AI workflow solutions from concept to pilot to production with measurable adoption and impact.
• Strong background in enterprise or solution architecture, with the ability to link AI capabilities to business processes, engineering workflows, data sources and enterprise systems.
• Ability to engage credibly with engineering leaders, architects, systems leads, subject matter experts, IT, data teams, suppliers and vendors.
• Experience creating technical frameworks, reference architectures, reusable patterns or platform enabled capabilities.
• Success in global, matrixed organisations driving alignment without relying on direct authority.

Technical Capabilities

• Deep practical expertise in enterprise AI architecture, including strong capability across most of the following areas:
• LLM and GenAI solution design, including model selection, prompt and context design, grounding, tool use, structured outputs and scalability trade-offs.
• Agentic AI, orchestration and human in the loop patterns.
• Classical and applied machine learning architecture, including supervised and unsupervised learning, predictive models, recommendation systems, similarity models, optimization, model validation, feature engineering and production deployment patterns for engineering and manufacturing use cases.
• Retrieval and knowledge systems, including RAG, embeddings, vector search, hybrid search, reranking, metadata and source traceability.
• AI evaluation, MLOps and lifecycle management, including test sets, hallucination testing, model and version governance, CI/CD, monitoring, drift detection, rollback, observability, feedback loops, support handover and continuous improvement.
• Secure and responsible AI architecture, including access control, data classification, data leakage prevention, prompt injection risk, secure model and API access, auditability, vendor risk, data residency, IP protection and safe use of proprietary engineering knowledge.
• AI platform and integration architecture, including cloud AI services, model gateways, orchestration tools, reusable services, APIs and supplier solutions.
• Integration of AI capabilities with engineering systems and data environments, such as PLM, CAD, simulation, manufacturing, quality or supply chain platforms.

Leadership and Working Style

• Leads as an enabler and technical authority, putting business unit needs first while ensuring solutions are scalable and enterprise aligned.
• Creates structure from ambiguous AI opportunities and turns them into practical roadmaps, solution patterns, decision points and delivery guidance.
• Applies systems thinking across tools, agents, models, data, workflows, platforms and enterprise systems.
• Balances innovation speed with enterprise controls, sustainable support models and production readiness.
• Constructively challenges designs that create duplication, unnecessary complexity, weak controls, poor scalability or vendor lock in.
• Guides multiple contributors through clarity, disciplined architecture and practical governance.

Competencies Values: Integrity, Accountability, Inclusion, Innovation, Teamwork