To apply for this job, you need to complete both steps below:

STEP 1:

Please click the link to submit your application directly to the company: 

Careers

Your application will only be received by Recruiter if submitted via above link. 

STEP 2:

Kindly scroll to the bottom of this page and complete the short VinUni Tracking Form.

Filling out this form alone does not count as applying. Kindly remind this form is not part of the company’s application process. It only helps Careers, Alumni, Industry and Development (CAID) Department discover more opportunities and follow up in case of system issues.

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.