Machine Learning Engineer

• Design, build, and optimize scalable, reusable ML platforms and production-grade ML systems
• Manage the end-to-end ML lifecycle, including model development, training, validation, deployment, monitoring, versioning, experiment tracking, reproducibility, and governance
• Build and maintain automated CI/CD/CT pipelines for model testing, validation, deployment, rollback, and retraining
• Use infrastructure-as-code and automated environment provisioning
• Design and support enterprise Feature Stores and Model Registries
• Partner with data engineering to build scalable training and inference data pipelines integrated with data warehouses, data lakes, and operational sources
• Develop batch and real-time inference frameworks, including low-latency event-driven scoring platforms
• Implement monitoring and alerting for model performance, data quality, drift, and system health
• Optimize ML systems for performance, reliability, maintainability, and cost efficiency
• Ensure MLOps governance and compliance with security, privacy, risk, and regulatory requirements
• Collaborate with data scientists, product owners, engineers, and business stakeholders
• Mentor junior engineers and drive MLOps standards and best practices

Explore Machine Learning Engineer at Veriday. Review requirements and application details.