AI Engineer

• Programming Languages: Proficiency in Python is essential, along with experience in other relevant languages such as R etc.
• Machine Learning Frameworks: Experience with frameworks such as TensorFlow, PyTorch, Keras, Scikit-learn, or XGBoost.
• Data Processing: Expertise in data manipulation and analysis using libraries like Pandas, NumPy, and Scipy.

• Algorithm Development: Ability to design, implement, and optimize machine learning algorithms (e.g., supervised/unsupervised learning, reinforcement learning).
• Deep Learning: Good to have knowledge of neural networks, CNNs, RNNs, GANs, and other deep learning techniques.
• Natural Language Processing (NLP): Good to have experience with NLP libraries such as NLTK, SpaCy, Hugging Face Transformers, or similar.
• Computer Vision: Good to have damiliarity with OpenCV, image processing, and computer vision techniques.
• Data Engineering: Good to have experience with ETL processes, data pipelines, and handling large datasets (e.g., Spark, Hadoop).
• Cloud Platforms: Good to have experience with cloud-based AI and machine learning services (e.g., AWS SageMaker, Google Cloud AI, Azure ML).

• DevOps/MLOps: Good to have experience in deploying and maintaining AI/ML models in production environments, including knowledge of Docker, Kubernetes, CI/CD pipelines.