Software Engineer - Machine Learning

Job Description

Company:Global Technology organization Key Skills: MLOps, NLP, Python, Data, Data Analysis Roles and Responsibilities: Design, build, and deploy machine learning models for healthcare applications, including medical image analysis, predictive analytics for patient outcomes, and natural language processing for clinical documents. Work with large-scale healthcare datasets such as electronic health records, imaging data, and genomics. Pre-process and cleanse healthcare data while ensuring compliance with HIPAA regulations. Collaborate with clinicians, data scientists, and software engineers to translate healthcare challenges into scalable ML solutions. Research and implement state-of-the-art ML/DL algorithms in the medical AI space. Optimize and validate models for fairness, explainability, and robustness. Publish findings and contribute to intellectual property, including patents and publications. Write clean, maintainable, and efficient code following best practices. Participate in code reviews, testing, and debugging. Optimize applications for maximum speed and scalability. Stay updated with emerging technologies and industry trends. Skills Required: 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. Education: BS/MS degree in Computer Science, Data Science, or a related field (PhD preferred).