• Design scalable, secure, and reliable infrastructure to support AI/ML workloads
• Build and implement MLOps pipelines to streamline model development, testing, and deployment
• Manage the end-to-end lifecycle of ML models, from development through retirement
• Set up monitoring and observability systems to track model performance, drift, and health in production
• Implement governance controls to ensure AI systems are compliant, auditable, and responsibly managed
• Integrate AI/ML platforms and models with existing enterprise systems and workflows

Requirements

• 6+ years of relevant experience in AI platform engineering, MLOps, or DevOps roles
• Strong hands-on experience in AI infrastructure architecture and design
• Proven expertise in MLOps implementation and CI/CD pipelines for ML
• Solid understanding of model lifecycle management practices
• Experience with model monitoring and observability tools and frameworks
• Familiarity with AI governance controls and compliance requirements
• Experience with integration with enterprise systems (APIs, data pipelines, cloud platforms)