About the role

Machine Learning Engineer at ExaCare AI developing novel machine learning solutions for healthcare challenges. Responsible for the end-to-end ML lifecycle, including dataset creation and model deployment.

Responsibilities

Research, design, and implement novel machine learning solutions using modern architectures to tackle complex business problems.
Build and manage efficient pipelines for rapid experimentation and hypothesis testing.
Methodically design, execute, and track all experiments, including hyperparameter searches, architecture changes, and data variations.
Deploy models into production environments using CI/CD practices and model serving frameworks.
Implement and maintain robust monitoring systems to track model performance, detect drift, and ensure reliability and scalability.
Apply modern techniques to optimize models for inference speed, memory footprint, and cost.
Lead efforts in dataset creation, augmentation, and curation to build high-quality, robust training data.
Stay current with and apply state-of-the-art techniques, especially relating to Large Language Models (LLMs).

Proven experience (3+ years) in building, training, and deploying machine learning models in a production environment.
Expert-level proficiency in Python
Experience with modern deep learning frameworks, such as PyTorch.
Demonstrable experience with systematic hyperparameter searching and optimization frameworks (e.g., Optuna, Ray Tune).
Exceptional organizational skills, with a strong emphasis on reproducible research and methodical experiment tracking.
Direct experience with LLMs, including fine-tuning, prompt engineering, RAG, and efficient inference.
Practical experience implementing model optimization techniques like quantization (e.g., bitsandbytes) and pruning.
Experience in designing and curating novel datasets from scratch.
Bachelor's or Master's degree in Computer Science, AI, Data Science, or a related technical field.