Work location- Paya Lebar
Build reusable MLOps workflows on AWS and Databricks using MLflow for experiment tracking, model registration, deployment, and retraining.
Develop and optimise PySpark and SQL pipelines on Delta Lake and AWS S3 to ingest, validate, and serve production datasets and ML features.
Turn Python prototypes into tested, Docker-containerised batch jobs, APIs, and model-serving endpoints.
Automate testing, deployment, and retraining with CI/CD and Terraform, with quality, security, and approval checks across environments.
Version code, data, features, prompts, and models, and maintain lineage, evaluation records, and rollback procedures for reproducible releases.
Monitor model performance, data drift, service reliability, and cloud costs, and troubleshoot production incidents across the data, model, and infrastructure layers.
The Company's principal activity is manpower contracting services with other information technology and computer services.