AI Transformation Engineer

For more than 80 years, our client’s engineers and product specialists have partnered with customers to produce highly engineered connectivity and sensing solutions that make a connected world possible. Their focus on reliability, durability, and sustainability exemplifies their commitment to progress. The unmatched range of their product portfolio enables companies, large and small, to turn ideas into technology that can transform how the world works and lives tomorrow.

Role Description:

• The AI Transformation Engineer is responsible for identifying, evaluating, and deploying AI-enabled solutions that improve engineering productivity, accelerate development activities, and enhance decision-making across ICT Engineering.
• The role serves as the bridge between engineering teams, AI technical specialists, and business stakeholders to ensure AI opportunities are translated into measurable business outcomes. This position leads the implementation lifecycle of approved AI initiatives, from opportunity assessment through deployment and value realization.
• The role focuses primarily on engineering workflows, knowledge work, and business processes where AI can eliminate non-value-added activities, improve engineering effectiveness, and reduce time-to-market.

Responsibilities:

Opportunity Identification & Assessment

• Partner with engineering teams to identify productivity challenges, workflow inefficiencies, and opportunities for AI-enabled improvement.
• Analyze current-state engineering processes and determine the potential value of AI solutions.
• Gather business requirements and define desired outcomes.
• Partner with AI technical specialists to assess technical feasibility and solution options.
• Work with the AI Value Realization Engineer to establish baseline measurements, value estimates, success criteria, and evidence requirements.
• Provide use-case requirements, value assumptions, and implementation estimates to support prioritization and business-case development.
• Determine whether identified opportunities are best addressed through AI, conventional automation, process improvement, or a combination of approaches.

Solution Delivery & Deployment

• Lead execution of approved AI initiatives from pilot through full deployment.
• Coordinate stakeholders across engineering, digital technology, AI development teams, and subject matter experts.
• Manage pilots, validation activities, user testing, and deployment readiness.
• Drive user adoption and organizational change management activities.
• Ensure solutions deliver expected business outcomes and user value.
• Rapidly prototype and evaluate AI-enabled workflows using available enterprise AI platforms, low-code/no-code tools, and workflow automation technologies.

Value Realization

• Own achievement of approved project objectives and targeted business benefits.
• Monitor adoption, usage, and performance of deployed solutions.
• Identify and resolve barriers to implementation success.
• Partner with the AI Value Realization Engineer to validate benefits and track realized value.

Requirements:

Experience & Qualifications

• Bachelor's degree in Engineering or related technical discipline.
• 5+ years of engineering, product development, manufacturing engineering, or related experience.
• Strong understanding of engineering workflows and development processes.
• Experience leading cross-functional projects and driving organizational change.
• Ability to translate business challenges into actionable improvement opportunities.
• Strong communication, facilitation, and stakeholder management skills.
• Demonstrated passion for AI and digital transformation.
• Practical experience applying AI, automation, or digital technologies to engineering or business problems; experience with generative AI, LLMs, AI agents, or workflow automation preferred.
• Experience rapidly prototyping and evaluating AI-enabled workflows using enterprise AI platforms, low-code/no-code tools, or workflow automation technologies.
• Working understanding of LLM capabilities and limitations, including common failure modes, validation approaches, and human-in-the-loop considerations.