Responsibilities
- Demonstrated background in evaluating and verifying AI and machine learning applications, including generative models, conversational agents, and predictive systems
- Solid grasp of the full lifecycle of machine learning model development, from data preprocessing to model tuning
- Familiarity with fundamental machine learning techniques such as regression, classification, clustering, decision trees, neural networks, and transformer architectures
- Proficient in using Python to automate testing workflows, analyze data, and validate model predictions
- Practical experience applying pandas, NumPy, and scikit-learn for assessing data integrity and model accuracy
- Proven ability in analyzing data quality, conducting data profiling, and validating input features
- Knowledge of performance evaluation criteria and methods to verify model outcomes
- Capable of interpreting how models make decisions and assessing outputs related to explainability
- Hands-on testing of AI-powered APIs and web services developed with FastAPI or Flask frameworks
- Exposure to deploying and validating AI models in cloud environments, with a preference for AWS SageMaker
- Understanding of operational aspects of machine learning in production, including deployment checks and ongoing monitoring
- Strong critical thinking, troubleshooting abilities, and clear communication for working with diverse teams
