Manager - Software Engineering

Caterpillar•Peoria, IL

5d•Onsite

About The Position

Manager – Engineering Transformation Engineering Transformation Manager leads a team responsible for embedding Artificial Intelligence into engineering development, validation, and quality processes. This role is accountable for delivering scalable, compliant, and high-impact AI-enabled solutions that improve productivity, quality, and cycle time across engineering organizations. The manager bridges AI technology, engineering execution, and regulatory compliance, ensuring that AI adoption is not only technically effective but also governed, auditable, and aligned to enterprise risk and quality standards

Requirements

10+ years of experience in software engineering, systems engineering, or engineering tools, with hands-on AI/ML application experience.
3+ years of experience in team leadership or supervisory roles.
Deep understanding of software development lifecycle, CI/CD, and validation processes.
Experience in vehicle/site connectivity, large dataset management
Strong ability to translate AI capabilities into business value and operational outcomes.

Nice To Haves

Experience leading AI transformation initiatives in validation, quality, or engineering domains.
Familiarity with functional safety standards (ISO 26262, IEC 61508, etc.)
Background in digital transformation and enterprise-scale implementations.

Responsibilities

Lead, mentor, and develop a team of engineering specialists focused on AI-enabled engineering and validation transformation.
Establish clear objectives, KPIs, and delivery roadmaps aligned to business outcomes (productivity, quality, cycle time).
Oversee execution of AI initiatives from concept through division scaling, ensuring measurable impact.
Direct development and deployment of AI-assisted solutions for: Code generation, review, and defect detection, Software development lifecycle optimization, Analytics automation (GenAI & Agentic AI)
Drive adoption of AI as a standard engineering capability rather than isolated experimentation.
Oversee application of AI to improve verification and validation processes , including: Test case generation and optimization, Requirements traceability, Defect prediction and quality insights
Ensure validation transformation aligns with quality, safety, and regulatory standards.
Identify and prioritize high-impact opportunities for AI-driven automation and workflow redesign.
Lead cross-functional initiatives to embed AI into core engineering processes.
Drive transition from pilot solutions to enterprise-wide adoption.
Collaborate with engineering leaders, product teams, and division stakeholders to align AI initiatives with business goals.
Communicate outcomes, risks, and roadmap to senior leadership.
Build organizational awareness and capability in AI through training, coaching, and best practices dissemination.