AI/ML Engineer

Overview

Design, build and deploy machine learning systems for the Insights & Personalization pod within TymeX, GoTymeX's technology hub for GoTyme's digital banking group, covering production ML across the full lifecycle.

What you'll do

• Design, build and optimize AI/ML pipelines for real-time and batch inference, using modern MLOps practices.
• Collaborate with data engineers and software developers to integrate models into TymeX's banking platform, ensuring reliability, monitoring and version control.
• Research, prototype and productionize models in areas such as credit scoring, fraud detection, transaction classification, personalization and conversational AI.
• Implement robust model evaluation, A/B testing and drift detection frameworks to ensure accuracy and stability over time.
• Contribute to internal frameworks and libraries that standardize ML development workflows across teams.
• Explore and evaluate emerging techniques in LLMs, Generative AI and reinforcement learning applicable to TymeX's ecosystem.
• Mentor junior engineers and collaborate closely with product and infrastructure teams to ensure model readiness for global scale.

What you'll need

• 5+ years of hands-on experience in machine learning engineering, data science, or related software development roles.
• Proven experience deploying and maintaining ML models in production environments (preferably in fintech, e-commerce, or large-scale consumer products).
• Strong proficiency in Python and core ML libraries (TensorFlow, PyTorch, Scikit-learn, XGBoost, etc.).
• Solid understanding of algorithms, data structures and distributed computing concepts.
• Experience with MLOps tools (e.g., MLflow, Kubeflow, Airflow, Docker, Kubernetes, CI/CD pipelines).
• Familiarity with data engineering stacks such as Spark, Kafka and cloud data warehouses (e.g., BigQuery, Redshift).
• Strong understanding of model monitoring, versioning and observability in live systems.
• A system-thinking approach to ML development, viewing models as part of a continuous delivery ecosystem.
• Curiosity for applied AI research balanced with pragmatism for production constraints.
• Strong communication skills and ability to collaborate in cross-functional and multi-country teams.

Nice to have

• Experience with cloud environments (AWS, GCP or Azure) and IaC (Terraform, CloudFormation).
• Familiarity with secure data handling and compliance within regulated environments (e.g., banking or financial data).

Details

• Hybrid working model, based out of the office in Vietnam; flexible working arrangements available for discussion.
• 40-hour workweek, Monday to Friday.
• Opportunities to travel overseas for training and work.