Backend Engineer

• Own the design and implementation of the internal ML platform, enabling end-to-end workflow orchestration, resource management, and automation using cloud-native technologies (GCP/Azure).
• Design and manage Kubernetes-based infrastructure for multi-tenant GPU and CPU workloads with strong isolation, quota control, and monitoring.
• Integrate and extend orchestration tools (Airflow, Kubeflow, Ray, Vertex AI, Azure ML or custom schedulers) to automate data processing, training, and deployment pipelines.
• Develop shared services for model behavior/performance tracking, data/datasets versioning, and artifact management (MLflow, DVC, or custom registries).
• Document architecture, policies, and operational runbooks to ensure platform maintainability and transparency.
• Contribute to CI/CD pipelines for ML models, integrating automated testing, deployment, and rollback mechanisms.
• Build reusable components for data ingestion, model training.
• Ensure compliance with data governance, security, and audit requirements.

Machine Learning Platform – Backend Engineer

Back-End Engineer – C++, TypeScript

Senior Java Developer