Sr. Engineering AI Enterprise Architect
TE Connectivity12 hours ago
London, United KingdomSenior
Responsibilities
- Define and maintain the engineering AI technical framework, including architecture principles, reusable patterns, readiness criteria, governance checkpoints, and lifecycle expectations.
- Assess local AI designs, identify reuse opportunities, industrialize successful prototypes, and guide solutions toward production readiness.
- Create and maintain the engineering AI technology roadmap across tools, agents, models, workflows, data products, integrations, and reusable services.
- Identify shared AI capabilities 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 protection, responsible AI, testing, documentation, monitoring, support, and production-readiness expectations into delivery standards.
- Guide multiple contributors and challenge duplication, unnecessary complexity, weak controls, poor scalability, and vendor lock-in.
Requirements
- Proven experience designing and scaling AI-enabled solutions in engineering or other complex operational environments.
- Experience serving as a senior AI technical authority by 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 through pilot to production.
- Strong enterprise or solution architecture background linking AI capabilities to business processes, engineering workflows, data sources, and enterprise systems.
- Deep expertise in LLM and GenAI solution design, agentic AI, orchestration, classical and applied ML architecture, retrieval and knowledge systems, and AI evaluation.
- Experience with MLOps and lifecycle management, including model governance, monitoring, drift detection, rollback, observability, feedback loops, and support handover.
- Knowledge of secure and responsible AI architecture, including access control, data classification, data leakage prevention, prompt injection risk, auditability, vendor risk, data residency, and IP protection.
- Experience with AI platform and integration architecture, cloud AI services, model gateways, reusable services, APIs, supplier solutions, and engineering-system integrations.
- Ability to work 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 in global, matrixed organizations.
Benefits
- Competitive salary package and performance-based bonus plans.
- Health and wellness incentives, employee stock purchase program, community outreach programs, charity events, and employee resource groups.
- The posting is associated with a remote role (#LI-REMOTE).
- Benefits may vary by site and may be provided by TE Connectivity or external service providers.
Categories
About TE Connectivity
TE Connectivity designs and manufactures connectors, sensors, and interconnect systems used by automotive, industrial, energy, aerospace, data center, and medical OEMs. It sells these components and related engineering services to equipment makers and system integrators worldwide, supporting applications from e-mobility and factory automation to communications infrastructure. The company is publicly traded as TE Connectivity plc (NYSE: TEL) and was previously known as Tyco Electronics after its 2007 separation from Tyco International.