AI Engineer II, Voicebot

Trung tâm Công nghệ Thông tin
Hồ Chí Minh
26-ITC-0471
MoMo is Vietnam’s leading mobile-payments platform, on a mission to make every transaction fast, easy, and joyful. In our AI & CreditTech team, Machine Learning is the engine behind the products millions of Vietnamese use every day: real-time credit scoring and loan decisioning, transaction verification, and AI-driven collections. We are looking for a Machine Learning Engineer who can own ML features end-to-end, from problem framing and modeling to shipping low-latency services in production and keeping them healthy.

Mô tả công việc

  • Design, build, and own ML solutions for fintech problems: credit scoring, decision and routing engines, fraud and transaction verification, recommendation, user segmentation, and recovery automation.

  • Take models from prototype to production, building and productionizing training and inference pipelines that serve real-time traffic at scale (sub-200ms latency, high availability).

  • Partner with Data Engineers, Analysts, Risk, and Product to turn business problems into measurable ML outcomes.

  • Run rigorous experimentation (A/B tests, backtesting, and simulation) and use the results to drive model and product decisions.

  • Monitor deployed models for drift, performance, and data-quality issues, owning diagnosis and iteration when metrics move.

  • Contribute to our shared ML platform, tooling, and MLOps practices, and help raise the engineering bar through code reviews and design discussions.

  • Explore applied GenAI and agentic systems (LLM-based assistants, retrieval, and workflow automation) where they add real value.

Yêu cầu công việc

  • 2–4 years of hands-on experience building and shipping ML systems to production (fintech, large-scale consumer, or real-time systems a strong plus).

  • Strong programming skills in Python (production-grade, not just notebooks); working knowledge of Java is a plus.

  • Practical experience with the modern ML/data stack: Scikit-learn (and/or PyTorch/TensorFlow), FastAPI for model serving, Airflow for orchestration, and Kafka / Spark / Lakehouse or BigQuery for data.

  • Solid grounding in probability, statistics, and algorithms, and sound judgment about model evaluation, validation, and trade-offs.

  • Comfortable owning a feature end-to-end and collaborating across Data, Risk, and Product, you communicate clearly and reason about business impact, not just model metrics.

  • Exposure to MLOps (monitoring, CI/CD for ML), experimentation frameworks, or applied LLM/agentic tooling (e.g., LangChain, vector databases) is a strong plus.