• 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.

• 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.

• 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.