Tóm tắt công việc
Build and operate production-grade machine learning systems end-to-end, including data ingestion, feature pipelines, training workflows, model serving, monitoring, retraining, and rollback.
Develop services, APIs, batch jobs, or real-time integrations that allow ML models to be used in product and operational workflows.
Work with Data Scientists and Data Engineers to productionize models, features, evaluation pipelines, and monitoring logic.
Build reliable ML infrastructure with strong focus on scalability, latency, observability, maintainability, and safe deployment.
Monitor production models for data drift, train/serve skew, prediction shifts, performance degradation, and system failures.
Contribute to model improvement through feature design, error analysis, evaluation, and production feedback.
Physical Wellbeing Benefit: General Insurance, Medical check-up, Accident Insurance, Healthcare Insurance
Emotional Wellbeing Benefit: Company Trip, Year End Party, Aha Hour Activities, Special Day Gifts, Aha Club (Badminton, Soccer).
Financial Wellbeing Benefit: Grab/Be For Work (Tech/Lead Level), Workplace Relocation, 13th Month Salary, PP Appreciate, Annual Leave Remain.

Strong software engineering skills, especially in Python, with experience building backend services, data pipelines, or production systems.
Good understanding of machine learning fundamentals, including supervised learning, feature engineering, model evaluation, and deployment.
Strong SQL skills and ability to work with large-scale datasets.
Experience with APIs, batch processing, streaming, workflow orchestration, CI/CD, monitoring, or cloud infrastructure.
Ability to debug issues across data, model, and system layers.
Comfortable working with Data Scientists, Backend Engineers, Product, and business teams to ship ML-powered features.
Willingness to go beyond pure engineering implementation and participate in data analysis, model evaluation, and business problem framing.
Nice to have
Experience with MLOps platforms, feature stores, model registries, experiment tracking, data quality checks, or model monitoring tools.
Experience with real-time prediction, ranking, pricing, fraud/risk, recommendation, dispatching, optimization, or marketplace systems.
Familiarity with A/B testing, backtesting, online evaluation, retraining pipelines, and business KPI measurement.
Experience with Kubernetes, Airflow, Kafka, Spark, BigQuery, PostgreSQL, Redis, Docker, or similar technologies.
Ability to read technical papers or references and turn them into practical production solutions.