Bangalore Analyst Ii Enterprise Systems Ka 560048

Job description

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 clear 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.