Machine Learning Engineer

We are seeking a talented and highly motivated Machine Learning Engineer to join our growing team. You will play a crucial role in designing, developing, and deploying robust AI models that power our innovative diagnostic solutions. This is an exciting opportunity to work with real-world medical data, contribute to life-saving technology, and directly impact patient care. Responsibilities: (1) Design, develop, and implement machine learning models (deep learning, computer vision, classical ML) for the analysis of complex medical data, including but not limited to medical images (histopathology, radiology), genomic data, and clinical records. (2) Pre-process, clean, and augment large datasets of medical images and other relevant data, ensuring data quality and readiness for model training. (3) Conduct extensive experimentation, evaluate model performance, and optimize algorithms for accuracy, robustness, and efficiency in a clinical context. (4) Collaborate closely with our medical experts, data scientists, and software engineers to translate research prototypes into production-ready solutions. (5) Develop and maintain scalable machine learning pipelines for data ingestion, model training, validation, and deployment. (6) Implement rigorous validation and testing protocols to ensure the safety, reliability, and regulatory compliance of AI models. (7) Stay up-to-date with the latest advancements in machine learning, deep learning, computer vision, and medical AI, and propose new technologies and methodologies. (8) Participate in code reviews, contribute to documentation, and promote best practices in MLOps and software development. Qualifications: (1) Bachelor's or Master's degree in Computer Science (More weightage to Specialization in AI/ML) (2) 1+ years of professional experience as a Machine Learning Engineer, Data Scientist, or similar role, with a strong focus on building and deploying ML models. (3) Proficiency in Python and strong experience with ML/DL frameworks such as TensorFlow, PyTorch, or Keras. (4) Solid understanding of machine learning algorithms, deep learning architectures (e.g., CNNs, Transformers), and computer vision techniques. (5) Experience with data preprocessing, feature engineering, and model evaluation metrics, especially in imbalanced datasets. (6) Familiarity with MLOps principles and tools (e.g., Docker, Kubernetes, AWS Sagemaker, Azure ML, GCP AI Platform). (7) Strong problem-solving skills, analytical thinking, and attention to detail. (8) Excellent communication and collaboration skills, with the ability to work effectively in a multidisciplinary team.