AIML delivery manager

Requirements

What You Will Do :

• Strategic Leadership : Define and execute the technical roadmap for AI/ML projects aligned with our healthcare goals, ensuring maximum impact and business value
• Team Management & Mentoring : Build, mentor, and lead a high-performing team of AI/ML professionals, fostering a culture of innovation, collaboration, and continuous learning
• End-to-End Solution Delivery : Oversee the entire MLOps lifecyclefrom data ingestion and model development to deployment and monitoringdelivering robust AI solutions for healthcare use cases
• Cross-Functional Collaboration : Partner with business leaders, healthcare domain experts, data science, and engineering teams to translate complex healthcare challenges into effective AI models, including real-time and batch processes
• Stakeholder Engagement : Serve as a trusted advisor and primary point of contact for key US and Indian stakeholders, ensuring transparency, alignment, and seamless communication
• Innovation & Best Practices : Keep abreast of emerging AI/ML trends and incorporate cutting-edge technologies and methodologies to accelerate innovation and improve healthcare outcomes
• Risk & Quality Management : Monitor project risks, escalate concerns proactively, and ensure adherence to timelines, scope, and quality standards
• Operational Excellence : Drive process improvements, optimize workflows, and establish scalable best practices for AI project delivery
  

Who You Are :

• Educational Background : Bachelor's in Engineering, Computer Science, or a related technical discipline. Advanced degrees are a plus
• Experience : 12-15 years in AI/ML, with a minimum of 2 years leading large-scale AI/ML projects in healthcare or health insurance
• Healthcare Expertise : Deep understanding of US health insurance industry or extensive experience working on healthcare AI solutions in India
• Technical Proficiency : Hands-on experience with cloud platforms (preferably Google Cloud Platform), MLOps pipelines, and deploying enterprise-level ML models
• Technical Skills : Strong grasp of AI/ML algorithms (supervised, unsupervised, reinforcement learning), model deployment, and scalable architecture design
• Leadership & Communication : Proven ability to lead diverse teams, influence stakeholders, and communicate complex ideas clearly
• Analytical & Process Skills : Skilled in risk management, process optimization, and continuous improvement