AIML Engineer

You are a skilled AI/ML Engineer with 46 years of hands-on experience in designing and deploying machine learning models, preferably within the clinical or healthcare domain. Your expertise will be instrumental in building impactful AI-driven healthcare solutions by leveraging your deep understanding of data science techniques, clinical data processing, and regulatory awareness. Your primary responsibilities will include designing, developing, and deploying ML/DL models tailored for clinical data applications such as EHR, imaging, and genomics. You will preprocess and clean large, complex datasets while ensuring compliance with healthcare data standards like HL7 and FHIR. Collaborating with clinical experts, data engineers, and product teams, you will help define ML problem statements and evaluate model performance using appropriate statistical and domain-specific metrics. In addition, you will be responsible for implementing pipelines for data ingestion, model training, and deployment in production environments, whether on the cloud or on-premise. It will be crucial for you to ensure that AI/ML solutions adhere to privacy, security, and ethical standards such as HIPAA and GDPR. Maintaining thorough documentation and contributing to knowledge sharing within the team will also be part of your role. To qualify for this position, you must hold a Bachelors or Masters degree in Computer Science, Biomedical Engineering, Data Science, or a related field. You should have a minimum of 46 years of experience in machine learning and data science roles, along with strong proficiency in Python and ML libraries like TensorFlow, PyTorch, and scikit-learn. Experience working with structured and unstructured clinical data, familiarity with clinical terminologies and healthcare interoperability standards, as well as hands-on experience with cloud platforms like AWS, GCP, or Azure are also required. Preferred qualifications for this role include prior experience in developing AI models for diagnosis, risk prediction, or clinical decision support. Knowledge of regulatory and ethical considerations in clinical AI systems, along with a track record of publications, patents, or contributions to open-source healthcare AI projects, would be advantageous.,