Senior AI/ML Specialist

Role & responsibilities

1. Technical Leadership & Strategy:

• Lead the end-to-end architecture, design, and development of advanced AI/ML models (e.g., Deep Learning, NLP, Computer Vision, Predictive Analytics) for healthcare applications.
• Define the technical vision and roadmap for AI projects, ensuring alignment with business goals and clinical needs.
• Provide technical guidance and mentorship to a team of data scientists and machine learning engineers while fostering innovation within the team.
• Stay at the forefront of AI research, evaluating and incorporating state-of-the-art techniques into our solutions.

2. Solution Design & Development:

• Architect and build ML pipelines for diverse healthcare data modalities, including Electronic Health Records (EHR), medical imaging (DICOM), genomics, wearables, and clinical notes.
• Develop and validate models for applications such as:
• Disease prediction and stratification
• Medical image analysis and diagnostics
• Clinical Natural Language Processing (NLP) for note summarization, phenotyping, and cohort identification.
• Drug discovery and biomarker identification
• Hospital operations and readmission risk prediction
• Ensure models are built with fairness, bias mitigation, and interpretability in mind.

3. Healthcare Domain Expertise & Compliance:

• Apply deep understanding of healthcare data standards (HL7, FHIR, DICOM) and clinical workflows to ensure solutions are practical and effective.
• Navigate the complexities of working with Protected Health Information (PHI) and ensure all solutions are designed with data privacy and security as a first principle (HIPAA, GDPR).
• Collaborate closely with clinicians, researchers, and regulatory affairs to ensure models are developed with a path towards clinical validation and regulatory approval (e.g., FDA SaMD) ensure real-world validation and compliance.

4. Execution & Deployment:

• Lead the MLOps lifecycle, from experimentation and training to deployment, monitoring, and continuous improvement of models in a production environment.
• Partner with software engineering, DevOps, and IT teams to integrate AI models into clinical applications and enterprise systems.
• Establish robust model monitoring frameworks to track performance, data drift, and concept drift in live settings.

Preferred candidate profile

1. Required Qualifications

• Education: Master's or Ph.D. in Computer Science, Data Science, Biomedical Informatics, Statistics, or a related quantitative field.
• Experience: 7-10 years of proven experience in designing, building, and deploying production-grade AI/ML solutions, with a minimum of 5 years focused specifically within the healthcare or life sciences domain.

2. Technical Skills:

• Proficient in Python, SQL, and frameworks like TensorFlow, PyTorch, Keras, and Scikit-learn.
• Strong understanding of healthcare data (EHR, DICOM) and standards (FHIR, HL7).
• Hands-on experience with MLOps tools (MLflow, Kubeflow, Airflow) and cloud platforms (AWS, GCP, Azure).
• Experience in NLP or Computer Vision applications within healthcare