Lead Agentic AI Engineer – Connectivity

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.

Define how AI agents are engineered and deployed across Caterpillar's connected-product ecosystem. As the Connectivity division's hands-on technical lead for agentic AI, the Lead AI Engineer will research emerging technologies, rapidly prototype promising approaches, and translate the best ideas into reusable engineering frameworks, reference architectures, and secure, enterprise-grade production solutions. Working at the intersection of agentic AI, software engineering, IoT, telematics, and connected machines, this role will improve how engineers build, diagnose, support, and operate connected products, partnering with Connectivity, Engineering, Cybersecurity, Enterprise AI, and business stakeholders across the full solution lifecycle, from use-case discovery and architecture through integration, evaluation, deployment, observability, governance, and value realization.

This is not a prompt-engineering-only or research-only role. You will write code, build prototypes, make architectural decisions, and establish engineering patterns that take AI systems from concept to production, while ensuring alignment with enterprise standards for security, compliance, performance, and responsible AI. Caterpillar will equip this role with modern AI engineering tools, including GitHub Copilot and current models and platforms from Anthropic, OpenAI, xAI, and other leading providers, based on enterprise approval and fit for purpose, as well as locally hosted and open models where they offer advantages in data control, latency, reliability, cost, or specialized performance. Experience in software engineering, cloud technologies, generative AI, agent-based architectures, and enterprise-scale solution delivery is essential.

What You Will Do:

• •AI Research and Rapid Prototyping: Evaluate emerging LLMs, agent frameworks, protocols, and locally deployable models, and build prototypes using tools such as GitHub Copilot and offerings from Anthropic, OpenAI, xAI, and other leading providers. Assess capability, cost, performance, security, and enterprise readiness to recommend Connectivity’s supported AI engineering stack.
• •Agentic Frameworks and Production Engineering: Develop reusable components for agent orchestration, context engineering, RAG, memory, tool use, MCP integration, multi-agent workflows, and human-in-the-loop controls. Design and build secure, maintainable, production-grade AI agents and services in Python, from prototype through deployment and operations.
• •Connected Product and System Integration: Apply AI to connected assets, telematics, embedded systems, device lifecycle management, remote services, and engineering workflows, integrating approved enterprise systems, APIs, documentation, and connectivity data while enforcing identity, access, and data-protection requirements.
• •Evaluation, Reliability, and Value Measurement: Create evaluation datasets, acceptance criteria, automated tests, tracing, and observability for agent behavior, retrieval quality, latency, and reliability. Measure gains in engineering productivity, troubleshooting, service responsiveness, quality, time, and cost to guide continued investment.
• •Technical Leadership and Responsible AI: Serve as Connectivity’s technical point of contact for agentic AI, establishing reference architectures and engineering practices teams can adopt consistently. Partner with enterprise architecture, cybersecurity, privacy, legal, and AI governance teams to ensure secure, auditable, and responsible AI patterns.

What You Will Have:

Application Design, Architecture: Knowledge of application design and architecture principles and practices; ability to utilize application design methodologies, tools, and techniques to convert business requirements and logical models into a technical application design.

• •Design and build secure, maintainable, production-grade AI agents and services in Python, including RAG, tool calling, context engineering, memory, MCP integration, multi-agent workflows, and human-in-the-loop controls.
• •Evaluate emerging LLMs, agent frameworks, protocols, and locally deployable models and develop prototypes using leading AI tools and platforms.
• •Experience taking software or AI solutions from prototype through production deployment and support.

Systems Thinking: Knowledge of the critical interdependencies among individual system elements that help and hinder performance; ability to plan and design solutions that synchronize resources to achieve business results.

• •Apply AI to connected assets, telematics, embedded systems, device lifecycle management, remote services, and engineering workflows.
• •Integrate enterprise systems, APIs, documentation, and connectivity data while considering identity, access, security, privacy, and data-protection requirements.
• •Partner with enterprise architecture, cybersecurity, privacy, legal, and AI governance teams to establish secure and responsible AI patterns.

Technical Troubleshooting: Knowledge of technical troubleshooting approaches, tools and techniques; ability to anticipate, recognize, and resolve technical issues on hardware, software, application or operation.

• •Create automated tests, tracing, and observability for agent behavior, retrieval quality, latency, and reliability.
• •Assess AI models and solutions for capability, performance, security, reliability, cost, and enterprise readiness.
• •Familiarity with cloud deployment, software testing, source control, and production monitoring.

Business Intelligence: Knowledge of business intelligence; ability to utilize business processes and technologies for gathering, storing, analyzing, and providing access to data that helps the organization make better business decisions.

• •Create evaluation datasets, acceptance criteria, and measurement approaches for AI solutions.
• •Measure gains in engineering productivity, troubleshooting, service responsiveness, quality, time, and cost to guide continued investment.
• •Translate business problems into technical solutions and explain results to technical and nontechnical stakeholders.

Considerations For Top Candidates:

• •Deep hands-on experience designing agentic AI systems using frameworks such as LangGraph, LangChain, Semantic Kernel, CrewAI, or similar technologies, including tool calling, RAG, memory, MCP, multi-agent orchestration, and human-in-the-loop patterns.
• •Experience evaluating, optimizing, and deploying open-weight or locally hosted LLMs, including model selection, inference optimization, security, observability, latency, and lifecycle management.
• •Experience architecting and operating AI workloads on Azure, AWS, or GCP using Docker, Kubernetes, infrastructure-as-code, CI/CD, and production monitoring.
• •Experience designing LLM evaluation and observability systems, including retrieval and response quality evaluation, automated testing, tracing, latency measurement, hallucination detection, and reliability monitoring.
• •Experience integrating AI agents with enterprise APIs, data sources, authentication and authorization systems, and production services.
• •Experience with IoT, telematics, embedded systems, connected equipment, or industrial digital platforms, including protocols and technologies such as MQTT, HTTPS, cellular, satellite, and device-to-cloud communication.
• •Experience rapidly evaluating emerging models and AI development platforms such as GitHub Copilot, Anthropic Claude, Op