AI/ML Engineer

· At least having 4-6 years of experience in AIML

· Machine Learning Algorithms: Understanding of machine learning algorithms, especially deep learning models like Transformers, is crucial. This can include various types of neural networks, optimization techniques, and loss functions.

· Natural Language Processing (NLP): Knowledge of NLP techniques, text representation methods, and language models.

· Programming Languages: Proficiency in languages commonly used in machine learning such as Python, R, Java, or Node.

· Library/Framework Proficiency: Familiarity with machine learning libraries like TensorFlow, PyTorch, Keras, or Scikit-learn.

· High-Performance Computing: Understanding of parallel computing, GPU acceleration, and distributed systems for training large models.

· Data Manipulation and Analysis: Skills in data preprocessing, transformation, and analysis are crucial, often involving libraries like Pandas, NumPy, or specialized tools for handling large datasets.

· DevOps and Infrastructure: Knowing how to set up and maintain machine learning pipelines, from data collection to model training to inference, possibly using cloud platforms like AWS, GCP, or Azure.

· Optimization: Understanding of both algorithmic and hardware optimization techniques to make training and inference more efficient.

· Version Control: Proficiency with version control systems like Git to manage codebase changes.

· Experience in REST API development, NoSQL database design, and RDBMS design and optimizations.

· Excellent problem-solving skills and a collaborative mindset.

· Strong communication skills to work effectively with diverse teams.