Demonstrates proficiency across model architecture, data pipeline integration, and the application, interpretation, and presentation of performance metrics. Maintains a strong understanding of foundational concepts in application development, infrastructure management, data engineering, and data governance.
Responsible for training, retraining, deploying, scheduling, monitoring, and continuously improving models based on iterative user and system feedback to deliver scalable, high-performance solutions. May lead geographically distributed teams and frequently serve as the primary point of contact for related technical matters, requiring strong analytical thinking, problem-solving ability, and exceptional communication skills.
Technical & Engineering Expertise
- Experience working with various ML libraries, packages, and frameworks
- Experience with standard machine learning frameworks such as: PyTorch or TensorFlow
- Design or select appropriate data and knowledge representation methods
- Recognize and apply software architecture, data modeling, and data structure principles
- Transform data science prototypes into scalable production solutions
- Provide system integration oversight
Core AI/ML Capabilities
- Select appropriate datasets for machine learning applications
- Perform statistical analysis and data interpretation
- Run and evaluate machine learning algorithms
- Use analytical results to improve and optimize models
- Train and retrain systems as required
- Research and implement a broad range of AI/ML algorithms and tools
- Verify data integrity and model output quality
- Identify data distribution differences that may impact model performance
