AI/ML Quality Engineer
Tasks
- Assure data integrity and quality
- Conduct engineering audit sessions
- Contribute to training programs
- Define AI quality and safety acceptance metrics
- Define and implement AI development processes
- Evaluate and qualify SW tools for AI development
- Facilitate risk assessment workshops
- Identify risks hazards failures
- Manage findings resolution plans
- Own competency management plans
- Promote safety and quality culture
- Review AI engineering standards
- Review AI related work products
- Steer data management frameworks
Perks/Benefits
Skills/Tech-stack
C plus plus | Convolutional Neural Network | DBT | Data Integrity | Data Pipelines | Data Quality | Git | GitLab | ISO 26262 | Keras | Neural Network | PyTorch | Python | Recurrent Neural Network | Snowflake | TensorFlow | Transformer | Unit Testing
Education
Bachelor of Engineering | Bachelor of Science | Master of Science | PhD
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