We are seeking a highly skilled Machine Learning Engineer to design, build, and optimize the core intelligence of our AI systems. You will be responsible for the end-to-end training process- from selecting the right architecture (CNNs, Transformers, etc.) to fine-tuning hyperparameters and managing GPU-accelerated training environments. Your work bridges the gap between raw data engineering and scalable deployment.

Key Responsibilities :

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).

Technical Qualifications :

1. Frameworks : Proficiency in PyTorch, TensorFlow, or JAX.

2. Mathematical Foundations : Strong understanding of linear algebra, calculus (for backpropagation), and statistical probability.

3. Hardware Acceleration : Experience with CUDA, NVIDIA Triton, or similar GPU-accelerated computing platforms.

4. Architectural Knowledge : Deep understanding of attention mechanisms, residual connections, and neural network optimization techniques.

Preferred Skills :

1. Experience transitioning models from the Data Modelling Layer into ML Ops pipelines.

2. Familiarity with training Large Language Models (LLMs) or generative architectures.