- Experience in testing and validating AI/ML solutions including Generative AI, Conversational AI, and Predictive models
- Strong understanding of how ML models are built end-to-end (data preparation, feature engineering, training, validation, tuning)
- Knowledge of core ML algorithms and model types (regression, classification, clustering, tree-based models, neural networks, transformers)
- Proficiency in Python for AI test automation, data analysis, and model output validation
- Hands-on experience with pandas, NumPy, and scikit-learn for data and model validation
- Experience in data quality analysis, profiling, and feature validation
- Understanding of model evaluation metrics and validation of performance results
- Ability to interpret model behavior and explainability outputs
- Experience testing AI APIs and services built using FastAPI or Flask
- Familiarity with cloud-based AI deployments,



preferably AWS SageMaker
- Understanding of production ML lifecycle, including deployment validation and monitoring
- Robust analytical, problem-solving, and communication skills for cross-functional collaboration

Desirable:
- Hands-on experience testing Generative AI prompts, hallucinations, and response quality
- Familiarity with Responsible AI, bias, fairness, and safety validation
- Knowledge of MLOps pipelines and CI/CD validation for ML systems Experience with performance, latency, and scalability testing for AI services
- Exposure to adversarial testing and edge-case validation for AI models
- Experience testing AI systems in regulated or high-risk domains

📌 QE AI Engg (Bengaluru)
🏢 Trigent Software
📍 Bengaluru