WORKFLOW TRANSFORMATION - AI & ENGINEERING

TE Connectivity Ltd.; Bengaluru; On-site; INR 1,500,000 - 2,100,000; Full time

Job summary

TE Connectivity Ltd. seeks a pragmatic, hands-on engineer who operates across software, AI, data and engineering systems to understand engineering problems, choose minimal viable tech, build the solution, and weave it into a broader technology ecosystem.

The role values delivering features over complexity and reusable solutions over isolated apps. The candidate will lead cross-functional transformation, promote digital engineering, automation and AI-enabled productivity, and identify use cases

Qualifications

• Strong understanding of PLM and product development processes.
• Experience implementing digital engineering and AI productivity solutions.
• Knowledge of AI best practices and governance.
• Ability to lead organizational change and transformation.
• Excellent stakeholder management across global teams.

Responsibilities

• Establish and govern engineering frameworks and standards across product teams.
• Drive improvements in operating models, workflows, and product development processes.
• Lead continuous improvement to enhance development speed, quality, and execution.
• Champion 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 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.

Job description

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

Job Overview

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.

Job Requirements

• 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.

What Your Background Should Look Like

• 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.

Competencies

SET : Strategy, Execution, Talent (for managers)