Machine Learning Engineer

We are looking for a curious and passionate Machine Learning Engineer to join our high-impact team. You’ll 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 You’ll Do :

• Design and Implement ML Models :

Develop, train, and evaluate machine learning and deep learning models for cardiac datasets and associated metadata.

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.

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).

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

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

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

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.

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).

Preferred Skills :