AI Automation Engineer

Veriipro; Dallas (TX); On-site; USD 120,000 - 160,000; Full time

Job summary

Veriipro is seeking an experienced AI Automation Engineer to build and optimize automated ML workflows, sensor-data pipelines, and dataset quality processes for computer vision and autonomous systems. You will work on Python-based tooling and end-to-end data and model workflows with cross-functional teams.

The role focuses on automating data collection from cameras and LiDAR, generating ground truth, and ensuring data integrity, labeling accuracy, and robust model evaluation.

Qualifications

• 6–10 years of relevant experience in AI/ML engineering, machine learning automation, computer vision, data engineering, or related fields.
• Demonstrated experience training, fine-tuning, evaluating, and deploying machine learning models.
• Hands-on experience generating, processing, and managing camera and LiDAR sensor datasets for perception or machine learning applications.
• Experience designing data collection pipelines, data preparation workflows, and ground truth generation processes.
• Strong experience in data annotation and quality assurance for autonomous systems, robotics, or computer vision projects.
• Proven ability to define annotation guidelines, identify labeling defects, and deliver measurable improvements in dataset quality and accuracy.
• Strong proficiency in Python for workflow automation, data processing, validation, and machine learning applications.
• Experience with computer vision datasets, model evaluation metrics, and performance analysis.
• Understanding of experiment tracking, dataset and model versioning, source control, and deployment practices.
• Ability to troubleshoot data pipeline issues, analyze model errors, and implement scalable automation solutions.
• Strong analytical, problem-solving, communication, and cross-functional collaboration skills.

Responsibilities

• Design, develop, and maintain automated pipelines for sensor-data ingestion, preprocessing, curation, sampling, annotation preparation, validation, and dataset release.
• Train, fine-tune, evaluate, and support the deployment of machine learning models for computer vision, perception, and autonomous systems applications.
• Develop data collection pipelines for camera and LiDAR sensors, ensuring data integrity, consistency, and suitability for machine learning workflows.
• Design and maintain ground truth generation workflows for object detection, classification, segmentation, tracking, and other perception tasks.
• Automate data annotation preparation, quality checks, and dataset validation processes.
• Define annotation guidelines, labeling standards, acceptance criteria, and automated checks to ensure label completeness, consistency, and accuracy.
• Identify labeling defects, dataset inconsistencies, and model-error patterns, and implement corrective actions to improve data quality.
• Develop Python-based tools to automate data validation, model evaluation, experiment execution, and machine learning workflows.
• Establish reporting and monitoring mechanisms for dataset health, annotation quality, model performance, and workflow throughput.
• Maintain dataset versions, experiment records, model artifacts, and release documentation to ensure traceability and reproducibility.
• Collaborate with data engineering, annotation operations, quality assurance, and AI/ML teams to improve end-to-end data and model workflows.
• Identify opportunities to reduce manual effort, improve processing efficiency, and scale AI automation capabilities.

Skills

Python development

ML engineering

Computer vision

Sensor data processing

Data annotation

Workflow automation

Experiment tracking

Version control

Cross-functional collaboration

Data quality

Tools

Airflow

Pandas

PyTorch

OpenCV

LiDAR tooling

Job description

We are seeking an experienced AI Automation Engineer to develop and optimize automated machine learning workflows, sensor-data pipelines, and dataset quality processes for computer vision and autonomous systems applications. The ideal candidate will have strong expertise in Python, machine learning model development, camera and LiDAR data processing, data annotation, and workflow automation.

Key Roles and Responsibilities

• Design, develop, and maintain automated pipelines for sensor-data ingestion, preprocessing, curation, sampling, annotation preparation, validation, and dataset release.
• Train, fine-tune, evaluate, and support the deployment of machine learning models for computer vision, perception, and autonomous systems applications.
• Develop data collection pipelines for camera and LiDAR sensors, ensuring data integrity, consistency, and suitability for machine learning workflows.
• Design and maintain ground truth generation workflows for object detection, classification, segmentation, tracking, and other perception tasks.
• Automate data annotation preparation, quality checks, and dataset validation processes.
• Define annotation guidelines, labeling standards, acceptance criteria, and automated checks to ensure label completeness, consistency, and accuracy.
• Identify labeling defects, dataset inconsistencies, and model-error patterns, and implement corrective actions to improve data quality.
• Develop Python-based tools to automate data validation, model evaluation, experiment execution, and machine learning workflows.
• Establish reporting and monitoring mechanisms for dataset health, annotation quality, model performance, and workflow throughput.
• Maintain dataset versions, experiment records, model artifacts, and release documentation to ensure traceability and reproducibility.
• Collaborate with data engineering, annotation operations, quality assurance, and AI/ML teams to improve end-to-end data and model workflows.
• Identify opportunities to reduce manual effort, improve processing efficiency, and scale AI automation capabilities.

Required Technical and Functional Skills

• 6–10 years of relevant experience in AI/ML engineering, machine learning automation, computer vision, data engineering, or related fields.
• Demonstrated experience training, fine-tuning, evaluating, and deploying machine learning models.
• Hands‑on experience generating, processing, and managing camera and LiDAR sensor datasets for perception or machine learning applications.
• Experience designing data collection pipelines, data preparation workflows, and ground truth generation processes.
• Strong experience in data annotation and quality assurance for autonomous systems, robotics, or computer vision projects.
• Proven ability to define annotation guidelines, identify labeling defects, and deliver measurable improvements in dataset quality and accuracy.
• Strong proficiency in Python for workflow automation, data processing, validation, and machine learning applications.
• Experience with computer vision datasets, model evaluation metrics, and performance analysis.
• Understanding of experiment tracking, dataset and model versioning, source control, and deployment practices.
• Ability to troubleshoot data pipeline issues, analyze model errors, and implement scalable automation solutions.
• Strong analytical, problem-solving, communication, and cross-functional collaboration skills.