machine learning.

- Build and maintain machine learning pipelines to deploy and maintain models in production;
Implement MLOps and LLMOps concepts to streamline the machine learning lifecycle;
Develop and maintain scalable, robust, and efficient machine learning infrastructure;
Collaborate with data science teams to understand model requirements and ensure successful integration into software products;
Communicate with different stakeholders such as data scientists, software engineers and cloud architects to ensure models are deployed and monitored in production environments according to best practices;

- Bachelor's or Master's in Computer Science, Engineering, or related field; 
Proficient in Python, with experience in machine learning frameworks like TensorFlow, PyTorch, or Scikit-learn; strong expertise in building and maintaining ML models and pipelines;
MLOps and Cloud Knowledge: Strong knowledge of MLOps and LLMOps concepts, with experience in cloud computing platforms, preferably Azure;
Good communication skills in English;