AI Engineer

Requisition Summary

PACCAR is seeking an experienced AI Engineer with a strong ability to build, optimize, and operationalize machine learning and AI systems across diverse data sources and business domains. The ideal candidate is enthusiastic about learning new AI technologies and applying them to empower internal customers and scale our intelligent solutions platform. The ideal candidate demonstrates strong business and communication skills and the ability to partner closely with Data Scientists, Data Engineers, Research teams, and business owners across both technical and non‑technical groups to define key business problems, then develop, deploy, and maintain the AI models that solve them.

In this role, you will serve as the expert in designing, implementing, and operating stable, scalable, and cost‑efficient machine learning pipelines—from feature engineering through model training, evaluation, deployment, and monitoring. Above all, you should be excited about leveraging advanced models, including classical ML, deep learning, and large language models, to answer business questions and drive measurable impact.

The AI Engineer will design, develop, implement, test, document, and maintain large‑scale, high‑performance AI systems that support analytics, automation, and intelligent applications. You will build and manage production‑grade model pipelines using best practices in MLOps across Azure ML, Databricks, AWS Sage Maker, or similar platforms. You will write efficient, scalable code and optimize model performance and inference workloads operating on large, complex datasets.

The person in this position should be analytical, have an extremely high level of customer focus, and a passion for continuous improvement. The AI Engineer should be a motivated self‑starter who can work independently in a fast‑paced, ambiguous environment and who brings excellent communication skills to collaborate with business stakeholders in defining problems, designing solutions, and validating real‑world outcomes.

• Design, implement, and support AI/ML infrastructure using Azure ML, Databricks, and related cloud services.
• Build, deploy, and maintain machine learning pipelines, including data preprocessing, feature engineering, model training, validation, and monitoring.
• Develop and operationalize models for prediction, classification, NLP, computer vision, or recommendation systems based on business needs.
• Use notebooks, experiment tracking, and visualization tools (e.g., Azure ML Studio, MLflow, Jupyter) to enable transparent, reproducible AI workflows.
• Provide support for Agile projects and deliver models through CI/CD and MLOps pipelines.
• Ensure responsible AI practices, including security, compliance, fairness, and model explainability, are embedded in all solutions.
• Work with business stakeholders to translate requirements into AI solutions and measurable outcomes.
• Optimize model performance, inference latency, training efficiency, and compute cost.
• Improve foundational AI/ML procedures, standards, and documentation across experimentation, deployment, and monitoring.
• Comply with change control and model governance processes.
• Maintain audit compliance for data, model lineage, and experiment traceability.
• Support scheduled after‑hours maintenance as needed.
• Ability to participate in an on‑call rotation for production AI systems.
• Perform additional AI‑and‑ML‑related tasks.
• Must meet physical requirements of the position with or without accommodation.
• Other duties as assigned.

• Bachelor’s degree in Computer Science, Data Science, Engineering, or related field.
• 2–5 years of experience in machine learning engineering, AI systems design, or applied ML.
• Strong knowledge of supervised/unsupervised learning, model evaluation, and feature engineering.
• Proficiency in Python and common ML frameworks (e.g., PyTorch, Tensor Flow, Scikit‑learn).
• Experience deploying and monitoring models in cloud environments (Azure ML, AWS Sage Maker, or GCP Vertex AI).
• Experience building automated ML pipelines using MLOps tools (MLflow, Kubeflow, Azure ML pipelines, Git Hub Actions).
• Familiarity with…