Role: Machine Learning Engineer (Databricks, MLflow)
Location- Petaling Jaya, Malaysia
Contract Duration: 12 Months
Key Skill Requirements
- Design, develop, test, deploy, and maintain scalable machine learning pipelines using Databricks, PySpark, and ML frameworks (e.g., Scikit-learn, XGBoost, TensorFlow, PyTorch).
- Collaborate with data scientists to productionize models on the Databricks platform and integrate them into business workflows.
- Build and optimize data pipelines for model training, inference, and evaluation, working closely with data engineers to access high-quality datasets stored in Delta Lake.
- Implement model monitoring, versioning, and retraining workflows to ensure model accuracy and relevance in production environments.
- Leverage MLOps tools on Databricks (e.g., MLflow, Databricks Model Serving, Feature Tables, Model Registry) to manage the full ML lifecycle.
- Optimize model inference performance and cost, including deployment strategies (batch, streaming, real-time APIs).
- Integrate machine learning models into business applications and dashboards, ensuring alignment with product and business requirements.
- Ensure data quality, model explainability, and traceability throughout the ML lifecycle.
- Participate in troubleshooting and resolving production issues related to machine learning systems.
- Collaborate with cross-functional teams to define success metrics, SLAs, and evaluation criteria for ML solutions.
- Contribute to documentation, coding standards, and best practices for ML engineering on Databricks.
Interested candidates can connect on +6586533349 (WhatsApp chat only)
