Job Title : Machine Learning Engineer
Role Overview
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
- 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.
- 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 :
- Supervised Learning (Classification, Regression).
- Unsupervised Learning (Clustering, Dimensionality Reduction).
- Reinforcement Learning (Agent-based decision making).
Technical Qualifications
- Frameworks : Proficiency in PyTorch, TensorFlow, or JAX.
- Mathematical Foundations : Strong understanding of linear algebra, calculus (for backpropagation), and statistical probability.
- Hardware Acceleration : Experience with CUDA, NVIDIA Triton, or similar GPU-accelerated computing platforms.
- Architectural Knowledge : Deep understanding of attention mechanisms, residual connections, and neural network optimization techniques.
Preferred Skills
- Experience transitioning models from the Data Modelling Layer into ML Ops pipelines.
- Familiarity with training Large Language Models (LLMs) or generative architectures.
(ref:hirist.tech)
