New opportunity

AVP, AI Engineer, Global Financial Market Technology, Group Technology

DBS Bank Ltd · Singapore

About this role

The AVP, AI Engineer will join the Group Technology function at DBS Bank, focusing on designing and developing advanced AI solutions for Global Financial Markets. The role involves building scalable, production-ready AI services and integrating them into enterprise systems. Responsibilities: Design and develop Agentic AI, Generative AI, and RAG solutions, including orchestration, tool calling, prompt engineering, memory, reflection, and evaluation. Build ML and Data Science models for client propensity, recommendations, classification, ranking, behavioral analytics, and next-best-action use cases. Analyze structured and unstructured data, perform data exploration, feature engineering, model evaluation, and performance monitoring. Develop scalable AI services and APIs, build reusable AI components, and implement monitoring, observability, and evaluation capabilities. Partner with stakeholders to translate business requirements into AI-powered solutions. Requirements: Hands-on experience in AI/ML Engineering, Data Science, or Advanced Analytics, with a track record of deploying solutions into production. Strong proficiency in Python, SQL, and REST APIs. Experience with ML frameworks such as Scikit-Learn, XGBoost/LightGBM, TensorFlow, or PyTorch. Mandatory experience in Generative AI covering RAG, Vector Search, LLM applications, Dify, and Agentic AI frameworks. Knowledge of data pipelines, data transformation, and data quality management. Experience with OpenShift (OCP), Docker, Kubernetes, CI/CD, and Git. Preferred: Experience in banking, financial services, capital markets, or wealth management. Advantageous: Experience in recommendation/personalization engines, search and retrieval, knowledge management, MLOps/LLMOps, AI governance, and enterprise document repositories.

For full details about this role, click “Apply on company site” to view the employer’s original posting.

Skills for this role

PythonSQLREST APIsScikit-LearnXGBoostLightGBMTensorFlowPyTorchRAGVector SearchLLM applicationsDifyAgentic AIData pipelinesFeature engineeringOpenShiftDockerKubernetesCI/CDGitMLOpsLLMOps

Checking your profile…

Preparation suggestions, not the employer’s actual interview questions. Always check the original posting for current requirements.

Understand the role

What matters most

Production-Grade Generative AI Development
The role requires designing and deploying Agentic AI, RAG, and LLM applications into production environments. You must demonstrate expertise in orchestration, prompt engineering, memory management, and reflection, ensuring these solutions are scalable and integrated with enterprise systems.
ML and Data Science Model Lifecycle Management
You will build and maintain models for client propensity, behavioral analytics, and next-best-action use cases. This includes the full lifecycle from data exploration and feature engineering to model evaluation, performance monitoring, and ongoing optimization.
Enterprise AI Infrastructure and Observability
A core priority is developing reusable AI components and APIs that adhere to banking standards. You must implement robust monitoring, observability, and evaluation frameworks to ensure AI solutions remain accurate, cost-effective, and performant within a Kubernetes/OpenShift environment.
Cross-Functional Stakeholder Collaboration
You must effectively partner with business stakeholders, product owners, and data scientists to translate complex Global Financial Markets requirements into actionable AI-powered solutions, ensuring technical outputs align with business objectives.

Likely challenges to think through

Balancing Innovation with Banking Compliance
Deploying advanced AI in a highly regulated financial environment requires strict adherence to AI governance and data quality standards. Prepare to discuss how you maintain model transparency, security, and compliance while implementing cutting-edge Agentic AI or RAG solutions.
Scaling AI Services in Complex Environments
Integrating AI services into existing enterprise systems often involves legacy data sources and complex infrastructure. Prepare by highlighting your experience with CI/CD pipelines, Docker, and Kubernetes to ensure your AI services are resilient and scalable.
Managing Unstructured Data Complexity
Financial markets generate vast amounts of unstructured data. You will face the challenge of effective feature engineering and retrieval accuracy in RAG systems. Prepare to explain your approach to data transformation and optimizing vector search for high-stakes financial decision-making.

Practice your answers

Technical practice

Designing RAG Architectures for Financial Data

What to coverExplain your approach to building a Retrieval-Augmented Generation system. Focus on how you handle vector search, data chunking strategies, and ensuring the accuracy of retrieved information when dealing with complex financial documents.

Agentic AI Orchestration and Tool Calling

What to coverDescribe how you design AI agents that utilize external tools. Discuss your methodology for prompt engineering, managing agent memory, and implementing reflection loops to improve task execution reliability.

Productionizing ML Models in Kubernetes

What to coverDetail your experience deploying models using Docker and OpenShift. Address how you manage container orchestration, scaling, and ensuring high availability for AI services in a production environment.

Feature Engineering for Behavioral Analytics

What to coverDiscuss your process for transforming raw data into features for behavioral analytics or propensity models. Explain how you ensure data quality and consistency across your pipelines.

Implementing MLOps and LLMOps Observability

What to coverHow do you monitor AI models and LLM applications in production? Describe the metrics you track for performance, drift, and cost, and how you integrate these into a CI/CD pipeline.

Model Evaluation Strategies

What to coverExplain your framework for evaluating Generative AI and traditional ML models. Contrast the metrics used for classification tasks versus those used to assess the quality and relevance of LLM-generated outputs.

Scalable API Development for AI Services

What to coverDescribe how you design REST APIs to serve AI models. Focus on handling concurrent requests, latency optimization, and integrating these services with existing enterprise financial systems.

Handling Unstructured Data in Financial Contexts

What to coverDiscuss techniques for extracting insights from unstructured data sources like enterprise document repositories. Suggest how to balance data privacy with the need for effective information retrieval.

Behavioural practice

Translating Business Requirements into AI Solutions

What to coverDescribe a time you worked with non-technical stakeholders to define an AI project. Explain how you translated their business needs into technical specifications and managed their expectations regarding model capabilities.

Navigating AI Governance and Compliance

What to coverProvide an example of how you have addressed AI governance or ethical considerations in a previous project. Discuss how you balanced innovation with the strict regulatory requirements typical of the financial sector.

Collaborating Across Technical Teams

What to coverShare an experience where you had to collaborate with data scientists and engineers to deliver a production-ready solution. How did you resolve technical disagreements or alignment issues during the development lifecycle?

Managing Project Trade-offs

What to coverDescribe a situation where you had to make a trade-off between model performance, latency, and cost. Explain your decision-making process and the impact of your choice on the final delivery.

Sharpen your knowledge

Topics to prepare

Agentic AI and Orchestration
Focus on the conceptual design of autonomous agents, including memory management, reflection, and tool-calling capabilities. Exercise: Draft a system architecture for a financial assistant that uses tool-calling to retrieve real-time market data and reflection to verify its own reasoning before responding to a user query.
RAG and Vector Search Optimization
Understand the end-to-end lifecycle of Retrieval-Augmented Generation, specifically data ingestion, chunking strategies, and vector database retrieval. Exercise: Compare different embedding models and chunking sizes to determine the impact on retrieval accuracy for unstructured financial documents.
MLOps and LLMOps for Production
Study the principles of model monitoring, observability, and performance evaluation in a production environment. Exercise: Design a monitoring dashboard strategy that tracks both traditional ML metrics like drift and LLM-specific metrics like hallucination rates and latency.

Skills to revise

Dify Framework Implementation
Gain hands-on experience with the Dify platform for building LLM applications. Exercise: Create a prototype workflow in Dify that integrates a custom knowledge base and a specific tool for data transformation to solve a common financial classification task.
Containerization and Orchestration with OpenShift/Kubernetes
Practice deploying Python-based AI services within containerized environments. Exercise: Write a Dockerfile for a FastAPI-based AI service and create a Kubernetes deployment manifest that includes resource limits and health checks suitable for an OpenShift environment.
Advanced Feature Engineering for Financial Models
Develop skills in transforming raw financial data into predictive features. Exercise: Build a pipeline using Python and SQL to aggregate behavioral data and generate features for a next-best-action recommendation model.

Your preparation checklist

Prepare a technical case study on RAG and Agentic AI
Develop a concise walkthrough of a production-grade RAG or Agentic AI system you have built. Focus on your approach to orchestration, tool calling, memory management, and evaluation metrics, as these are explicitly required for the role.
Map your MLOps and LLMOps experience to enterprise requirements
Prepare to discuss how you have implemented monitoring, observability, and CI/CD pipelines for AI models. Highlight your experience with containerization (Docker/Kubernetes) and OpenShift, specifically how you ensure scalability and performance in production environments.
Review financial domain applications for AI
Research common AI use cases in capital markets and wealth management, such as client propensity modeling, behavioral analytics, and next-best-action engines. Be ready to explain how you would adapt your data science skills to solve these specific financial business problems.
Formulate questions regarding AI governance and infrastructure
Prepare thoughtful questions about how DBS handles AI governance, data quality management, and the integration of AI services into their existing enterprise systems. This demonstrates your understanding of the complexities of deploying AI in a highly regulated banking environment.

Availability last checked: 18 Oct 2026. Status can change after a check; confirm on the employer’s website. Report an issue · Our checking policy

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