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.