This is a position at Qode's Client
About The Role
We're looking for an AI/ML Engineer to join our Insights \& Personalization pod, a team building the intelligence layer that makes digital banking adaptive, transparent, and deeply personalized. This role is perfect for someone who understands that production ML in financial services isn't just about model accuracy; it's about building trustworthy, explainable systems that customers can rely on with their money.You'll design and deploy machine learning systems across the full stack, from transaction intelligence and behavioral pattern recognition to predictive forecasting, conversational AI, and agentic solutions. You'll be building production systems that customers interact with directly, making the impact of your work tangible and immediate. You'll build systems that create compounding value as they learn from more data and usage, with the opportunity to see your work scale globally across our digital banking platforms.
What You'll Do
As an AI/ML Engineer, you'll work across the full ML lifecycle, from model design and experimentation to large-scale deployment and performance optimization, within a distributed, microservices-based, and cloud-native environment.You will:
Design, build, and optimize AI/ML pipelines for real-time and batch inference, leveraging modern MLOps practices
Collaborate with data engineers and software developers to integrate models into the 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 to standardize ML development workflows across teams
Explore and evaluate emerging techniques in LLMs, Generative AI, and reinforcement learning applicable to the ecosystem
Mentor junior engineers and collaborate closely with product and infrastructure teams to ensure model readiness for global scale
Requirements
What You'll Bring
Experience:
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)
Technical Skills:
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
Experience with cloud environments (AWS, GCP, or Azure) and IaC (Terraform, CloudFormation) is a plus
Familiarity with secure data handling and compliance within regulated environments (e.g., banking or financial data) is advantageous
Mindset:
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
Benefits
Meal and parking allowance
Benefits and salary rank during probation
Insurances as Vietnamese labor law and premium health care for you and your family
Performance bonus up to 2 months
13th month salary
15-day annual leave+ 3-day sick leave + 1 birthday leave + 1 Christmas leave
Work-life balance 40-hr per week from Mon to Fri
Opportunity to build large-scale AI systems that power global digital banking across multiple markets
Work in a cloud-native, event-driven, and API-first architecture with cutting-edge technologies
Join a team where AI is not an experiment; it's a core part of the platform strategy
Hybrid working model, strong engineering culture, and continuous learning environment (LLMs, MLOps, data observability, and more)
Be part of a global technology organization with shared ownership and impact