Manager; AI Engineering

Career Area:
Technology, Digital and Data

Job Description:

Your Work Shapes the World at Caterpillar Inc. When you join Caterpillar, you're joining a global team who cares not just about the work we do - but also about each other. We are the makers, problem solvers, and future world builders who are creating stronger, more sustainable communities. We don't just talk about progress and innovation here - we make it happen, with our customers, where we work and live.

Together, we are building a better world, so we can all enjoy living in it.

Cat Digital is the digital and technology arm of Caterpillar Inc., leveraging the latest technologies to build industry leading digital solutions for our customers and dealers. With over 1.5 million connected assets worldwide, our teams use data, technology, advanced analytics, telematics and AI capabilities to help our customers build a better, more sustainable world.

We are seeking an experienced Software Engineering Manager to lead a team responsible for designing, building, and operating cloud-native AI agents and tools. In this role, you will provide technical and organizational leadership for a team developing enterprise-scale AI Assistant that transform how Caterpillar customers, dealers, and employees interact with digital products. You will oversee the end-to-end delivery of AI-powered applications and services, including agentic based architecture, agent orchestration framework and tools services, and observability solutions.

The ideal candidate combines strong people leadership with hands-on technical expertise in modern software engineering and Generative AI technologies.

Lead and develop a high-performing team of software engineers responsible for AI agent and AI tooling development. Drive technical strategy, architecture, and execution for cloud-native AI solutions. Oversee the design, development, deployment, and operation of AI agents, agent orchestration, and supporting services. Partner with product owners, architects, data scientists, business stakeholders, and engineering teams to define and deliver AI-enabled capabilities. Establish engineering best practices, coding standards, testing frameworks, security controls, and operational excellence processes.

Own service reliability, scalability, performance, and production support for AI applications and platforms. Lead release planning, resource allocation, project execution, and risk management across multiple initiatives. Drive continuous improvement in engineering productivity, software quality, observability, and automation. Develop team capabilities through coaching, mentoring, career development, and succession planning. Foster a culture of innovation, experimentation, collaboration, and accountability. Ensure compliance with enterprise security, governance, and responsible AI requirements.

Organizational Leadership:
Proven ability to lead software engineering teams and deliver results through others. Experience building highly engaged teams and developing engineering talent.

Software Development:
Deep understanding of modern software engineering principles, design patterns, and development practices. Experience leading the delivery of distributed cloud-native applications.

Software Architecture:
Experience designing scalable, resilient, and maintainable software platforms. Ability to evaluate architectural tradeoffs and guide technical decisions.

Software Quality & Operations:
Experience establishing testing strategies, observability standards, monitoring practices, and incident management processes. Knowledge of reliability engineering, security best practices, and operational excellence.

Proven experience leading engineering teams. Experience building AI assistants, digital copilots, autonomous agents, or AI-powered business applications. Experience with AI evaluation, model observability, prompt management, and experimentation frameworks. Experience working with cloud AI services and leading foundation models such as OpenAI, Anthropic Claude, Gemini, or open-source alternatives. Understanding of LLMs, RAG architecture, tool-calling patterns, prompt engineering, and agent orchestration frameworks. Familiarity with frameworks such as Lang Graph, Lang Chain, Semantic Kernel, CrewAI, or similar technologies.

Experience building and operating applications on major cloud platforms such as AWS, Azure, or Google Cloud. Knowledge of containerization, CI/CD pipelines, infrastructure-as-code, and platform operations. Strong proficiency in Python, Go, Java, or similar programming languages. Strong knowledge of Agile development Experience working in Azure Dev Ops.

Pay Range: $ - $ Compensation and benefits offered may vary depending on multiple individualized factors, job level, market location, job-related knowledge, skills, individual performance and experience. Please note that salary is only one component of total…