Software Engineer - Machine Learning

You will be responsible for designing, building, and deploying machine learning models for healthcare applications in a Global Technology organization. Your key responsibilities will include: - Designing and deploying machine learning models for healthcare applications like medical image analysis, predictive analytics for patient outcomes, and natural language processing for clinical documents. - Working with large-scale healthcare datasets such as electronic health records, imaging data, and genomics. - Pre-processing and cleansing healthcare data while ensuring compliance with HIPAA regulations. - Collaborating with clinicians, data scientists, and software engineers to translate healthcare challenges into scalable ML solutions. - Researching and implementing state-of-the-art ML/DL algorithms in the medical AI space. - Optimizing and validating models for fairness, explainability, and robustness. - Publishing findings and contributing to intellectual property, including patents and publications. - Writing clean, maintainable, and efficient code following best practices. - Participating in code reviews, testing, and debugging. - Optimizing applications for maximum speed and scalability. - Staying updated with emerging technologies and industry trends. Your skills should include: - Strong programming expertise in Python and familiarity with ML frameworks such as TensorFlow, PyTorch, or Scikit-learn. - Hands-on experience in Natural Language Processing (NLP) and Deep Learning techniques. - Experience with MLOps practices -- model deployment, monitoring, and versioning using tools like MLflow, Kubeflow, or Airflow. - Solid understanding of data preprocessing, data pipelines, and data analysis. - Knowledge of cloud platforms (AWS, Azure, or GCP) for ML model deployment. - Familiarity with healthcare data standards (e.g., HL7, FHIR, DICOM) is an advantage. - Excellent analytical, problem-solving, and communication skills. - Ability to collaborate effectively in multidisciplinary teams and work in a fast-paced environment. Qualifications required for this role include: - BS/MS degree in Computer Science, Data Science, or a related field (PhD preferred).,