Sr R&D/Product DVL Engineer

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

• + Establish and govern engineering frameworks, best practices, and development standards across product teams.
• Drive improvements in engineering operating models, workflows, and product development processes.
• Lead continuous improvement initiatives to enhance development speed, quality, and execution effectiveness.
• Champion the adoption of digital engineering, automation, and AI-enabled productivity solutions.
• Identify and prioritize use cases for Traditional AI, Generative AI, and Agentic AI to improve engineering and business outcomes.
• Promote documentation standards, knowledge management, modular architectures, and design reuse strategies.
• Lead cross-functional transformation initiatives and foster collaboration across engineering organizations.
• Drive adoption of digital engineering and AI-enabled tools to improve productivity and design efficiency.

• Strong understanding of engineering product development processes and Product Lifecycle Management (PLM).
• Experience implementing digital engineering, automation, and AI-enabled productivity solutions.
• Understanding of AI best practices, including Generative AI, Agentic AI, governance and enterprise adoption frameworks.
• Ability to identify, evaluate, and implement use cases across Traditional AI, Generative AI, and Agentic AI.
• Ability to lead organizational change and process transformation initiatives.
• Strong  leadership through infuence, communication, and stakeholder management skills across global teams.
• Ability to balance strategic transformation objectives with practical implementation and scalable adoption.

SET : Strategy, Execution, Talent (for managers)

The ideal candidate is a pragmatic, hands-on engineer who is comfortable working across software, AI, data, and engineering systems.

They should be able to understand an engineering problem, identify the minimum effective technology needed to solve it, build the solution, and integrate it into a broader technology ecosystem.

They should value working features over unnecessary complexity, reusable solutions over isolated applications, and measurable business or engineering outcomes over technology experimentation.