Ne design, train, and deploy predictive and generative machine learning models. The technical workflow includes establishing data pipelines (ETL/ELT), selecting model architectures (e.g., Transformers, GANS, CNNs) using Frameworks like Tensorflow or PyTorch, and executing training, validation, and hyperparameter tuning. Deployment methods include cloud-based inference, edge optimization, and MLOps practices for monitoring, data drift detection, and automated model retraining

We engineer AI solutions to automate workflows. This involves analyzing processes, identifying automation points, and developing custom agents or integrating Al services (e.g., NLP, Computer Vision). Implementation focuses on workflow orchestration, API integrations, handling exceptions, and building scalable systems with monitoring and auditing