Machine Learning Engineer

1. Data Modelling & Training Process :

- Optimization : Implement and optimize training algorithms using Backpropagation and Gradient Descent variants.

- Loss Function Design : Define and customize Loss Functions tailored to specific business objectives and data distributions.

- Hyperparameter Tuning : Conduct systematic Hyperparameter tuning to maximize model performance and generalization.

- Compute Management : Oversee efficient GPU training workflows, ensuring optimal resource utilization and reduced training latency.

- Refinement : Execute Fine-tuning strategies on pre-trained models to adapt them for specialized downstream tasks.

2. Machine Learning Layer Architecture :

- Deep Learning : Design and implement advanced architectures, including Convolutional Neural Networks (CNN) for computer vision and Deep Neural Networks (DNN) for complex pattern recognition.

- State-of-the-Art Models : Build and scale Transformers for natural language processing and sequence-to-sequence tasks.

- Paradigm Expertise : Develop systems across various learning paradigms, including :

a. Supervised Learning (Classification, Regression).

b. Unsupervised Learning (Clustering, Dimensionality Reduction).

c. Reinforcement Learning (Agent-based decision making).