We are looking for a curious and passionate Machine Learning Engineer to join our high-impact team. Youll work directly on ML models that analyze cardiac data, helping doctors save lives.

This role offers the unique opportunity to see your work make a tangible difference in patient outcomes while building state-of-the-art ML infrastructure.

What Youll Do :

- Design and Implement ML Models : Develop, train, and evaluate machine learning and deep learning models for cardiac datasets and associated metadata.

- Data Pipeline Development : Work with large, complex, and sometimes messy clinical datasets. Contribute to building robust and scalable data pipelines for data ingestion, cleaning, feature engineering, and labeling.

- Model Deployment and MLOps : Deploy models to production environments and monitor their performance in real-world clinical settings.

- Build and maintain REST APIs for model inference using frameworks like FastAPI or Flask. Design scalable API endpoints with proper request validation, error handling, and authentication.

- Implement and maintain robust MLOps practices, including version control, continuous integration/continuous deployment (CI/CD), and model monitoring in a production environment (e.g., cloud platforms like AWS, Azure, or GCP).

- Performance Optimization : Optimize model performance for inference speed and resource efficiency, crucial for deployment on various platforms (cloud, edge devices).

- Collaboration : Work collaboratively with software engineers, data scientists, and clinical domain experts to translate clinical needs into technical requirements and deliver high-impact solutions.

- Documentation and Research : Maintain detailed documentation of models, code, and experiments. Stay current with the latest research in deep learning, medical image analysis, and time-series analysis.

- Regulatory and Compliance : Develop documentation in order to comply with regulatory requirements such as CDSCO, FDA etc.

Required :

- Experience : 1-3 years of professional experience as a Machine Learning Engineer, Data Scientist, or a related role.

- Programming : Proficiency in Python and experience with core data science libraries (NumPy, pandas, scikit-learn).

- Deep Learning Frameworks : Hands-on experience with at least one major deep learning framework (PyTorch).

- MLOps Basics : Familiarity with MLOps principles, including containerization (Docker) and cloud service experience (AWS, Azure, or GCP).

- Understanding of model evaluation metrics, cross-validation, and debugging ML systems.

- Ability to read and implement research papers.

Preferred Skills :

- Experience with healthcare data.

- Understanding of statistical methods and experimental design for model validation.

- Experience with structured training pipelines such as Pytorch Lightning.

- Knowledge of regulatory requirements for medical devices (FDA, CE marking).

- Experience with cloud platforms (AWS, GCP, Azure) and serverless deployments.

- Publications or contributions to ML open-source projects.