AI Automation Engineer

Must Have Technical/Functional Skills

Demonstrated experience training, fine-tuning, and deploying machine learning models.

Demonstrated experience generating and managing camera and LiDAR sensor datasets for

perception or machine learning applications.

Experience designing data collection pipelines and ground truth generation workflows.

Demonstrated experience in data annotation and quality assurance for autonomous systems or

computer vision projects.

Demonstrated ability to define annotation guidelines, identify labeling defects, and deliver measurable

improvements in dataset accuracy.

Strong Python skills and experience automating data validation, model evaluation, and machine

learning workflows.

Understanding of computer vision datasets, model metrics, experiment tracking, version control, and

deployment practices.

Roles & Responsibilities

Build automation for sensor-data ingestion, curation, sampling, annotation preparation, validation, and

release.

Train, fine-tune, evaluate, and support deployment of machine learning models for computer vision or

perception use cases.

Design camera and LiDAR data collection and ground truth generation pipelines.

Define annotation guidelines, acceptance criteria, and automated checks for label completeness,

consistency, and accuracy.

Analyze labeling defects and model-error patterns, then implement corrective actions and measurable

quality improvements.

Create reporting for dataset health, annotation quality, model performance, and workflow throughput.

Collaborate with data engineering, annotation operations, quality, and AI teams.

Generic Managerial Skills,

Strong technical ownership, analytical problem solving, collaborative delivery, and clear communication

with cross-functional teams