Senior Executive / Assistant Manager

Roles and Responsibilities Design and build reproducible ML pipelines for training, evaluation, and deployment using Cloud ML platforms. Implement CI/CD pipelines for ML models using cloud-native services (Cloud Build for GCP, CodePipeline for AWS, Azure DevOps for Azure). Deploy and manage models using cloud-native services (Vertex AI, SageMaker, Azure ML) with proper IAM, encryption, and security controls. Set up automated monitoring for model drift, performance degradation, and data quality issues. Maintain model registry, versioning, and lineage tracking across development and production environments. Successfully deploy and maintain all requested models in production, ensuring smooth collaboration with data scientists for production-ready code refactoring and optimization. Establish automated retraining mechanisms for production models. Work across the cloud ML infrastructure to reduce model deployment times significantly. Requirements Bachelor's degree or above in Computer Science, Software Engineering, or a related field. Minimum 3 years of experience in Data Science development, including at least 1 year dedicated to MLOps. Strong Python programming skills, with hands-on experience in managed ML platforms (Vertex AI, SageMaker, or Azure ML; GCP preferred). Proficiency in DevOps practices, infrastructure-as-code (Terraform), containerization (Docker), and orchestration (Kubernetes). Experience implementing CI/CD pipelines and monitoring stacks (Prometheus, Grafana, Cloud Monitoring). Solid foundation in mathematics, statistics, and machine learning principles, along with agile/scrum collaboration tools (GitHub, Jira). Strong problem-solving abilities and excellent communication skills with proficiency in English working environment; knowledge of Logistics or Supply Chain Management is a plus.