Principal Architect - AI Factory (Physical AI)

Caterpillar Inc.•Irving, TX

8d•$159,120 - $258,570•Onsite

About The Position

The Principal Architect for AI Factory defines the data architecture that enables Physical AI and autonomous systems to learn, reason, and operate at scale. The role establishes data domains, canonical data models, data products, and architectural standards to turn jobsite, machine, sensor, telemetry, simulation, and operational data into trusted, reusable assets for AI development and autonomy. Working across data, AI, digital twins, and autonomy, the architect leads how data is organized, managed, and delivered across the enterprise so engineers and data scientists can develop, train, fine-tune, validate, and deploy AI models efficiently. The role aims to accelerate innovation through scalable, reusable data products and create the data foundation for AI innovation at scale. This position requires full-time work at Caterpillar's Irving, Texas office. Domestic relocation assistance and visa sponsorship are available.

Requirements

Knowledge of data architecture processes, techniques, and factors, with the ability to design blueprints integrating data resources for business processes and functional support.
Knowledge of analytical techniques and tools, with the ability to identify organizational problems' root causes and create alternative solutions.
Knowledge of platform architecture technologies and methods, with the ability to design processing mechanisms and roadmaps for business application systems and deploy interfaces in accordance with standards and processes.
Knowledge of requirements analysis tools, methods, and techniques, with the ability to elicit, analyze, and record functional and non-functional business requirements.
Knowledge of target architecture and the ability to develop IT blueprints and roadmaps aligned with business strategies and objectives.
Bachelor's degree in Computer Science, Computer Engineering, or a related field.
Work full-time at the Irving, TX office.

Nice To Haves

Typically 8+ years architecting distributed AI infrastructure on-premises and in the cloud, including multi-node GPU clusters, high-speed interconnect fabrics, high-throughput AI storage, and cluster sizing.
Deep Kubernetes experience for GPU workloads, plus Slurm, Run:ai, NVIDIA Mission Control, or Ray; infrastructure as code with Terraform, Ansible, or Helm; and hybrid cloud/on-premises environments.
Experience with distributed training and performance tools and methods, including PyTorch FSDP, TensorRT, DeepSpeed, Megatron, NCCL, profiling, parallelism, and elastic or fault-tolerant training.
Experience building data flywheels for petabyte-scale multimodal sensor data, including ingestion, lakehouse/object storage, distributed ETL, curation, labeling, dataset versioning and lineage, and synthetic data integration.
Experience with MLOps and model lifecycle workflows, including training, fine-tuning, evaluation, registry, deployment, monitoring, governance, and VLA or foundation-model post-training.
Experience with simulation-in-the-loop and reinforcement learning infrastructure, synthetic data pipelines, and large-scale closed-loop simulation or replay-based evaluation.
Experience with the NVIDIA ecosystem and edge deployment, including tools such as DGX, NVIDIA AI Enterprise, NeMo, NIM, Triton, TensorRT, Dynamo, CUDA, and Jetson or DRIVE-class edge compute.
Experience with observability, reliability, and security practices, including DCGM, Prometheus, Grafana, Loki, SRE practices, SLOs, GPU cost management, and security and governance for safety-relevant autonomy software.
Strong Python and working C++ skills; experience with containers, CI/CD, Bazel or monorepo tooling, AWS or Azure, and build-versus-buy decisions.
Architecture leadership translating architecture into implementation plans, reference architectures, and standards across platform teams, and partnering with Cat Digital Division and NVIDIA or cloud providers.
Domain exposure to ML infrastructure or data/compute platforms for autonomous vehicles or robotics, or AI Factory reference architectures.
Master's or doctoral degree preferred.

Responsibilities

Develop detailed architecture deliverables to solve business problems.
Design application technical infrastructure, including databases, programming languages, utilities, and testing approaches.
Lead evaluation and deployment of new technologies that add to or enhance existing digital technical capabilities.
Help address application business requirements and collaborate with cross-functional teams to deliver digital solutions that meet business results.
Establish data domains, canonical data models, data products, and architectural standards for Physical AI and autonomous systems.
Provide technical leadership for organizing, managing, and delivering data across the enterprise.