AI Platform Engineer

Caterpillar Inc.

. Build and operate scalable AI data, training, evaluation, and MLOps infrastructure .

Posted 11/4/2026full-timeWuxi • ChinaMid-LevelSeniorWebsite

Core Competencies

Role fit

Core Competencies

Use this summary to align your resume positioning with the role.

Demonstrates expertise in building and operating scalable AI data infrastructure, including MLOps and model lifecycle management. Proficient in Python and familiar with machine learning frameworks and data processing tools essential for autonomous vehicle and mining-site applications.

Highest-signal resume keywords

MLOps Infrastructure DevelopmentPython ProficiencyMachine Learning FrameworksData Processing and StorageModel Lifecycle Management

ATS Keywords

Tailor your resume

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills

Software EngineeringData Platform DevelopmentMachine Learning Model EvaluationExperiment TrackingData VersioningDeployment Pipeline ManagementDistributed TrainingModel RegistryData Quality MechanismsCross-Team Collaboration

Soft Skills

Problem AnalysisCross-Team Communication

Tools & Technologies

KubernetesDockerMLflowRayAirflowKubeflowSageMakerCI/CD

Certifications & Qualifications

Master's Degree in Computer ScienceMaster's Degree in Artificial IntelligenceMaster's Degree in Software EngineeringMaster's Degree in Data EngineeringMaster's Degree in Automation

Industry Keywords

Autonomous DrivingRoboticsIndustrial AIMining DataMultimodal DataReinforcement LearningSensor DataData Governance

Tech Stack

Tools & technologies

AirflowCyber SecurityDockerKubernetesPythonPyTorchRayTensorflow

About the role

Key responsibilities & impact

Build and operate scalable AI data, training, evaluation, and MLOps infrastructure
Support perception, prediction, VLA, and scheduling-optimization teams in developing, validating, and managing AI models
Build data collection, ingestion, processing, and storage capabilities for autonomous vehicle and mining-site data
Establish dataset versioning, metadata, annotation workflows, and data-quality mechanisms
Manage sensor data, vehicle status, actions, operational data, simulation data, Corner Cases, Failure Cases, Demonstration Data, and multimodal Vision-Language-Action data
Build reusable training jobs, compute scheduling, experiment tracking, and automated evaluation capabilities
Support traditional ML, deep learning, VLM/VLA, and back-office optimization model development
Build Model Registry, Artifact management, and model lifecycle processes
Maintain traceability among code, data, configuration, checkpoints, models, and evaluation results
Provide model packaging, candidate-version management, Deployment Pipeline Infrastructure, and rollback mechanisms
Improve pipeline stability, scalability, resource utilization, and developer experience
Establish monitoring, alerting, and issue-diagnosis capabilities
Collaborate with perception, prediction, planning, VLA, scheduling optimization, AI Runtime, Validation, Cybersecurity, and Functional Safety teams
Convert common cross-team requirements into standardized platform capabilities

Requirements

What you’ll need

Master's degree or above in Computer Science, Artificial Intelligence, Software Engineering, Data Engineering, Automation, or a related field
5-8 years of software, data platform, machine learning platform, or MLOps research and development experience
Ability to independently own the design and delivery of key platform modules
Proficiency in Python and strong software engineering capabilities
Familiarity with at least one mainstream machine learning framework, such as PyTorch or TensorFlow
Familiarity with data processing, distributed training, experiment tracking, Model Registry, model evaluation, and CI/CD
Familiarity with one or more of Kubernetes, Docker, MLflow, Ray, Airflow, Kubeflow, SageMaker, or similar platforms
Understanding of engineering requirements for data and model versioning, Lineage, reproducible training, Deployment Pipeline, and production environment monitoring
Good cross-team communication and problem-analysis skills
Ability to communicate technically in English
Preferred: experience with autonomous driving, robotics, industrial AI, or mining non-road-scenario data and MLOps platforms
Preferred: experience with VLM/VLA multimodal data, Demonstration Data, simulation data, or reinforcement-learning training platforms
Preferred: experience with large-scale sensor data, annotation platforms, data quality, or data governance
Preferred: experience with LLM/LLMOps, model post-training, or AI governance

Benefits

Comp & perks

Equal Opportunity Employer
Qualified applicants of any age are encouraged to apply