AI & Engineering Transformation Consultant - Digital Engineering & R&D

Role Overview

The role operates at the intersection of engineering domain expertise, artificial intelligence, and hands-on solutioning. The successful candidate will engage directly with clients R&D, design, and

manufacturing functions to understand how work is performed - the processes, people, and systems involved and translate that understanding into AI and automation solutions, from ideation to solution design.

The position combines advisory capability with delivery ownership, requiring both domain know-how and technical execution.

Key Responsibilities

1. Advise clients across the automotive, aerospace, life sciences, and manufacturing sectors on AI adoption, intelligent automation, and engineering and PLM transformation to drive innovation and business value.

2. Support and deliver Product Lifecycle Management and product development transformation engagements, including requirement gathering, functional process optimization and solution design across

the engineering value chain.

1. Embed with client teams to run AI discovery workshops, map complex process and data workflows, and align solution architecture with business outcomes.

2. Analyse and map industry workflows end to end, capturing As-Is and To-Be states, conducting gap analysis, and designing solutions aligned to client objectives and engagement scope.

3. Act as the primary point of contact for the client on AI engagements, building trusted relationships and helping teams move quickly towards most viable solution.

4. Translate complex, real-world business process challenges and user needs into structured AI and automation solutions that improves efficiency and embed intelligence into engineering workflows.

5. Scope, design, and shape AI and automation solutions from opportunity definition and ideation through to solution design and proof-of-concept.

6. Proactively identify additional automation and AI opportunities within client processes that can unlock further value.

7. Collaborate closely with client teams to understand core business challenges and take ownership of the technical success of the engagement.

8. Bridge domain and software, translating engineering and R&D requirements into implementable technical solutions grounded in an understanding of real industrial operations.

9. Develop assets, accelerators, reusable playbooks and points of view on emerging trends, including Generative AI, agentic AI, digital twins, and IoT, in support of internal and client initiatives.

10. Lead stakeholder management, facilitate workshops, and operate effectively across multi-cultural and multi-geography environments.

Functional Focus

1. Project and product management

2. New Product Development

3. Engineering transformation

4. Process mapping

5. Product/portfolio standardization and modularization

6. Knowledge management

7. Stakeholder management

8. Workshop facilitation

Must have:

1. Business and engineering consulting experience.

2. Demonstrated experience in delivering AI and/or intelligent automation projects, ideally in a client-facing capacity.

3. Core industry experience in: automotive, aerospace, life sciences, or discrete/process manufacturing, with a sound understanding of the associated business processes.

4. Robust product development and R&D experience.

5. Strong functional knowledge of at least one Product Lifecycle Management (PLM) suite.

6. Proficiency in business process mapping and value-stream mapping (e.g. Visio, Signavio).

7. Know how of Agile methodologies (SAFe, Scrum, Kanban) and Software Development Lifecycle (SDLC).

8. Strong project management, requirement gathering, and stakeholder engagement skills.

Good to have:

1. Generative AI and agentic AI project experience.

2. Experience across engineering transformation domains: process/project transformation, PLM software transformation, manufacturing transformation, or servitization/service transformation.

3. Design for X (Value, Six Sigma, Cost).

4. Exposure to digital twins, product/portfolio standardization and modularization, and knowledge management.

5. Familiarity with Agile tools (Jira, Confluence) and cloud platforms.

Education

Bachelors degree in engineering, preferably in Mechanical, Automotive, Production, Electronics, or Industrial disciplines.

An MBA or PGDM qualification is an added advantage.