• Architect and lead the development of large-scale machine learning platforms and services that support model training, deployment, and lifecycle management.
• Collaborate with Data Science, Applied Science, and Product teams to productionize ML models with performance, reliability, and compliance in mind.
• Define and implement MLOps best practices, including model versioning, automated retraining, monitoring, and CI/CD workflows.
• Lead technical decision-making and platform evolution to ensure scalability, security, and maintainability of ML infrastructure.
• Optimize inference systems for real-time and batch applications across cloud and hybrid environments.
• Serve as a technical advisor to leadership and engineering teams on architectural decisions and ML infrastructure investments.
• Mentor and develop senior-level engineers across teams and contribute to technical capability building across the organization.
• Evaluate and incorporate new technologies and frameworks to advance the ML engineering roadmap.