Strong understanding of statistical methods, ML algorithms, and deep learning architectures.
Must be proficient in Python and have experience with machine learning libraries and frameworks such as TensorFlow, PyTorch, or Keras.
Must be familiar with cloud-based platforms and services, such as AWS, GCP, or Azure.
Need to have experience with natural language processing (NLP) techniques and tools, such as SpaCy, NLTK, or Hugging Face.
Knowledge of model interpretability and fairness frameworks (SHAP, LIME, Fairlearn) and responsible AI principles.
The best bullet to remove is the cloud platform experience bullet because it's largely covered by the more specific bullet about building and deploying ML models at scale in cloud environments.
Revised list:
Implement generative AI models, identify insights that can be used to drive business decisions. Work closely with multi-functional teams to understand business problems, develop hypotheses, and test those hypotheses with data, collaborating with cross-functional teams to define AI project requirements and objectives, ensuring alignment with overall business goals.
Optimizing existing generative AI models for improved performance, scalability, and efficiency.
Leading the design and development of prompt engineering strategies and techniques to optimize the performance and output of our GenAI models.
Implementing cutting-edge NLP techniques and prompt engineering methodologies to enhance the capabilities and efficiency of our GenAI models.
Hands-on experience building and deploying ML models at scale in cloud environments (GCP Vertex AI, AWS SageMaker, Azure ML).
