Artificial Intelligence

Prepare data pipelines and develop data models to support machine learning workflows and ensure that the right data is available for training and deploying AI models.

Implement best practices and toolchains (e.g., MLflow, Kubeflow, AWS SageMaker, Azure ML) to automate the end-to-end machine learning lifecycle—from data preparation and model training to deployment and monitoring.

Build advanced models to forecast demand, predict equipment failures, or anticipate market trends. Typical approaches may involve time-series analysis, regression models, or deep learning.

Create vision-based AI models for image classification, object detection, facial recognition, and more. Can include OCR (Optical Character Recognition) for document processing.

Develop and deploy chatbots, voice assistants, and text analytics solutions using state-of-the-art NLP techniques and large language models (e.g., GPT-based systems).

Provide services involving large language models (LLMs), text-to-image generation, or other generative techniques. This may include customization, fine-tuning, or integration of pre-trained generative models.