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Job Overview

Support Engineering AI initiatives through use-case development, user acceptance testing (UAT), user training, adoption, and support. Apply Creo, PDMLink, and engineering-process expertise to validate solutions and improve engineering productivity.

Job Requirements

- AI use cases: Identify, document, prioritize, and validate engineering AI use cases in partnership with engineering users and stakeholders.
- User acceptance testing: Prepare representative test scenarios, execute UAT, document results, track defects, and coordinate issue resolution.
- Training and user support: Create user guides and learning materials, conduct training and demonstrations, support users, and capture structured feedback.
- Engineering systems expertise: Apply hands-on Creo, PDMLink, and mechanical-engineering process knowledge to assess solution fit, usability, and business relevance.
- Adoption and change support: Support communications, onboarding, readiness activities, feedback loops, and adoption campaigns for Engineering AI capabilities.
- Productivity improvement: Support process optimization, establish baseline measures, and track productivity and user-experience benefits.
- Collaboration and documentation: Work with DES, business units,



technology teams, and solution providers while maintaining clear use-case, test, training, and support documentation.

Required Qualifications and Experience:

- Education: Bachelor's degree in Mechanical Engineering or an equivalent engineering discipline.
- Experience: 3 to 5 years of relevant experience in mechanical engineering, engineering systems, digital engineering, or application support.
- Technical expertise: Hands-on experience with Creo, PDMLink/Windchill, and mechanical-engineering or product-development processes.
- Testing and validation: Experience preparing test scenarios, conducting UAT, documenting results, and coordinating defect resolution.
- Training and support: Ability to create explicit user documentation, deliver training, support users, and translate feedback into practical improvements.
- Improvement methods: Exposure to Lean, process mapping, automation, analytics, or similar productivity-improvement methods.
- Core skills: Strong problem-solving, communication, documentation, training, and cross-functional collaboration skills.

Preferred Qualifications:

- Exposure to Engineering AI, digital engineering, AI-assisted design, or engineering automation.




- Experience supporting pilots, proof-of-concepts, application rollouts, or adoption initiatives.
- Familiarity with engineering data, design-review workflows, and enterprise application support processes.

Success Measures:

- Delivery: Use cases and UAT activities completed with clear documentation, traceable results, and stakeholder alignment.
- Adoption: Users trained, supported, and enabled to apply Engineering AI capabilities effectively.
- Quality: Issues, feedback, and improvement actions documented and progressed through appropriate resolution channels.
- Impact: Engineering productivity and user-experience improvements identified, measured, and demonstrated.

Career Path:

Potential progression into Engineering Systems SME, Engineering AI Product Analyst, Product Owner, or Digital Engineering lead roles within DES.

ABOUT TE CONNECTIVITY

TE Connectivity plc (NYSE: TEL) is a global industrial technology leader creating a safer, sustainable, productive, and connected future. As a trusted innovation partner, our broad range of connectivity and sensor solutions enable the distribution of power, signal and data to advance next-generation transportation, energy networks, automated factories, data centers enabling artificial intelligence, and more.

Our more than 90,000 employees, including 10,000 engineers, work alongside customers in approximately 130 countries. In a world that is racing ahead, TE ensures that EVERY CONNECTION COUNTS. Learn more at and on LinkedIn, Facebook,

📌 ANALYST II ENTERPRISE SYSTEMS (Bengaluru)
🏢 TE Connectivity
📍 Bengaluru