AI Engineer

Expert-level Python development with additional exposure to R, Java, or C++.

Strong ML/DL knowledge, including supervised, unsupervised, and reinforcement learning, plus experience with CNN and RNN architectures.

Hands-on experience with AI/ML frameworks such as TensorFlow, PyTorch, Keras, and scikit-learn.

Data processing and feature engineering expertise, including data cleaning, transformation, and model validation.

Database proficiency in both SQL and NoSQL environments.

Big data experience using tools like Apache Spark and OpenSearch for large-scale data handling.

Solid mathematical and statistical foundation, including linear algebra, probability, calculus, and statistical modeling.

Cloud and MLOps experience, deploying and managing models on AWS, Azure, or Google Cloud Platform using best practices for CI/CD and model lifecycle management.