AI Platform Engineer

Programming

Languages:

Mastery of Python is essential, with R, Java, and C++ also being highly valuable.

Machine Learning (ML) & Deep Learning (DL):
You'll need a deep understanding of ML concepts (supervised, unsupervised, reinforcement learning) and neural network architectures like CNNs and RNNs.

AI/ML Frameworks and Libraries:
Proficiency is required in tools like Tensor Flow, PyTorch, Keras, and scikit-learn.

Data Science and Analysis:
Skills in data acquisition, cleaning, preprocessing, and feature engineering are crucial, along with knowledge of SQL and No

SQL databases.

Big Data Technologies:
Familiarity with platforms like Apache Spark and Open Search is often necessary for handling large-scale data.

Mathematics and Statistics: A strong foundation in linear algebra, calculus, probability, and statistics is fundamental.

Natural Language Processing (NLP):
For language-based AI, expertise in NLP techniques and libraries such as NLTK, spaCy, and Hugging Face Transformers is key.

Cloud Computing and MLOps:
Knowledge of cloud platforms (AWS, GCP, Azure) and MLOps principles is vital for deploying and managing AI models.