Lead AI/ML Engineer (Healthcare)

AI Project Execution & Delivery:

• Lead the end-to-end execution of high-priority AI/ML projects, ensuring they are delivered on time, within budget, and to the highest technical standards.
• Serve as the primary technical point of contact for project stakeholders, managing dependencies, mitigating risks, and communicating progress effectively.

AI Governance & AIRB Facilitation:

• Manage the day-to-day operations of the AI Review Board (AIRB) submission process, acting as a hands-on guide for Data Science and product teams.

Team Leadership & Technical Mentorship:

• Provide direct line management, technical leadership, and mentorship to a team of senior AI/ML Engineers and Data Scientists.
• Conduct code reviews, design sessions, and technical deep dives to ensure the quality, scalability, and robustness of AI solutions.

Hands-on MLOps & Engineering Practice:

• Drive the practical implementation of the MLOps strategy, directly overseeing the construction and optimization of CI/CD pipelines for AI/ML systems using tools like GitHub Actions.

Required Qualifications:

• Proven AI/ML Leadership: 10-15 years of experience in the AI/ML field, with at least 4-5 years in a leadership or management role leading technical teams in the delivery of complex AI solutions.
• Experience with AI Governance: Direct, hands-on experience successfully navigating an internal AI ethics, risk, or governance review process for multiple projects.
• Strong Project Management Skills: Demonstrated ability to manage complex technical projects from conception to deployment, with expertise in agile methodologies.
• Expertise in the ML Lifecycle: Deep, practical knowledge of the entire machine learning lifecycle, from data acquisition and feature engineering to model deployment and post-launch monitoring.
• Hands-on MLOps Experience: Proven experience building and managing CI/CD pipelines and MLOps workflows for machine learning.
• Strong Technical Foundation: Proficient in Python, common ML frameworks (e.g., TensorFlow, PyTorch, scikit-learn), and cloud platforms (AWS, Azure, or Google Cloud Platform).