Artificial Intelligence Engineer

Role & responsibilities

Design, develop, and deploy ML/DL models tailored for clinical data applications (e.g., EHR, imaging, genomics).
Preprocess and clean large, complex datasets, ensuring compliance with healthcare data standards (e.g., HL7, FHIR).Collaborate with clinical experts, data engineers, and product teams to understand and define ML problem statements.Evaluate model performance using appropriate statistical and domain-specific metrics.Implement pipelines for data ingestion, model training, and deployment in production environments (cloud/on-premise).Ensure AI/ML solutions adhere to privacy, security, and ethical standards (HIPAA, GDPR, etc.).Maintain thorough documentation and contribute to knowledge sharing within the team.Required Qualifications:Bachelors or Master’s degree in Computer Science, Biomedical Engineering, Data Science, or a related field.4–6 years of experience in machine learning and data science roles.Strong proficiency in Python and ML libraries (TensorFlow, PyTorch, scikit-learn, etc.).Experience working with structured and unstructured clinical data.Familiarity with clinical terminologies (e.g., SNOMED CT, ICD-10) and healthcare interoperability standards.Experience with cloud platforms (AWS, GCP, or Azure) for ML model development and deployment.Preferred Qualifications:Prior experience in developing AI models for diagnosis, risk prediction, or clinical decision support.Knowledge of regulatory and ethical considerations in clinical AI systems.Publications, patents, or contributions to open-source healthcare AI projects.

Preferred candidate profile

We are seeking a skilled AI/ML Engineer with 4–6 years of hands-on experience in designing and deploying machine learning models, preferably within the clinical or healthcare domain. The ideal candidate will bring a deep understanding of data science techniques, clinical data processing, and regulatory awareness to help build impactful AI-driven healthcare solutions.