Lead Machine Learning Engineer

Key Responsibilities:

• Team Leadership & Hiring:
• Lead recruitment efforts for Machine Learning Engineers (MLEs) within GCC IN.
• Support talent acquisition for other Data Technology functions.
• Build and retain a high-performing team of ML Engineers.
• Coaching & Development:
• Mentor and coach team members to foster technical excellence.
• Cultivate a high-impact, collaborative engineering culture.
• ML Standards & Best Practices:
• Contribute to the definition and implementation of ML standards within Haleon's AI leadership team.
• Promote engineering best practices across the ML lifecycle.
• Project Portfolio Management:
• Coordinate and manage competing priorities across Haleon's AI project portfolio.
• Ensure timely, scalable delivery of AI solutions.
• Model Productionisation & Deployment:
• Lead efforts to scale ML models from prototype to production.
• Oversee robust, secure, and automated data pipelines and monitoring frameworks.
• Performance Monitoring & KPIs:
• Define and manage KPIs and diagnostics to evaluate model performance.
• Align model outputs with business objectives.
• Responsible AI Implementation:
• Ensure model development adheres to Haleon's Responsible AI policy.
• Lead initiatives for explainability, bias mitigation, security, and documentation.
• AI Cost & Sustainability:
• Manage production costs and promote sustainability across the AI portfolio.
• Optimize infrastructure and operational efficiency of deployed ML solutions.
• Vendor & Partner Management:
• Build and maintain strategic relationships with 3rd-party ML Ops vendors and technology partners.

Minimum Qualifications:

• Experience:
• 13+ years total professional experience.
• At least 5 years in a Machine Learning Engineering (MLE) role.
• Minimum 2 years in a technical leadership position.
• Education:
• Bachelor's degree (BSc) in Mathematics, Science, Computational Engineering, or a related field.

Preferred Qualifications:

• Experience:
• 8+ years in an MLE role, including 4+ years in a leadership capacity.
• Education:
• Master's degree (MSc) or Ph.D. in a quantitative or engineering discipline.