New opportunity

VP, Lending Platform Technology Lead, Institutional Banking Group Technology, DBS Singapore

DBS Bank Ltd · Singapore

About this role

The VP, Lending Platform Technology Lead will provide end-to-end technology leadership for DBS’s Lending platforms, covering engineering strategy, architecture, modernisation, delivery, and operational resilience. The role oversees technical leadership across Credit Origination, Credit Processing, Loan Servicing, and the broader Lending ecosystem for both Institutional and Consumer Banking groups. Responsibilities include defining platform strategy, driving cloud-native modernisation, and leading the design and implementation of GenAI and Agentic AI solutions to enhance customer experience and operational efficiency. The leader will build a strong engineering culture, manage technical debt, and ensure compliance with risk, security, and regulatory requirements. Required qualifications include a minimum of 10 years of technology experience with substantial hands-on software engineering, a proven track record in banking or financial services, and deep expertise in Java, Spring Boot, and microservices. Preferred candidates will have experience delivering AI-enabled solutions, including LLMs, RAG, and AI agents. A Bachelor or Master degree in Computer Science, Engineering, or IT is required.

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

Skills for this role

JavaSpring BootMicroservicesAPI DevelopmentKafkaEvent-driven ArchitectureCloud-nativeOpenShiftDevSecOpsCI/CDGenerative AIAgentic AILLMRAGEnterprise Architecture

Your skill match

Checking your profile…

YOUR NEXT STEP

Get interview-ready for this role

A focused preparation guide, built around this job’s responsibilities and requirements.

✦ AI-generated guide
Preparation suggestions, not the employer’s actual interview questions. Always check the original posting for current requirements.
01

Understand the role

What matters most

  • End-to-End Lending Platform Modernization

    The role requires driving the modernization of credit origination, processing, and loan servicing platforms. You must balance the migration to cloud-native, microservices-based architectures with the need for operational resilience and the management of existing technical debt.

  • Integration of Generative and Agentic AI

    A core priority is the industrialization of AI solutions within the lending lifecycle. This involves moving beyond experimentation to implementing LLMs, RAG, and intelligent workflow orchestration to enhance productivity and customer experience while maintaining strict regulatory compliance.

  • Technical Leadership and Engineering Culture

    You are expected to provide hands-on technical guidance, including source-code level reviews, while leading senior engineering teams. Building a culture of accountability and continuous learning is essential to support the delivery of mission-critical banking systems.

Likely challenges to think through

  • Balancing Innovation with Regulatory Rigor

    Implementing cutting-edge AI and modernizing legacy lending systems creates a tension between rapid innovation and the bank's stringent requirements for risk management, data governance, and cybersecurity. Prepare to discuss how you maintain velocity without compromising compliance.

  • Managing Complex Stakeholder Ecosystems

    The lending platform serves both Institutional and Consumer Banking groups, requiring alignment across diverse business, operations, and credit risk teams. You will face the challenge of reconciling conflicting priorities while driving cross-team technical alignment.

03

Practice your answers

Technical practice

Modernizing Legacy Lending Platforms

What to coverDiscuss your approach to migrating monolithic lending systems to a cloud-native, microservices-based architecture. Focus on strategies for maintaining operational resilience, managing technical debt, and ensuring zero-downtime deployments using OpenShift or similar container platforms.

Implementing GenAI and Agentic AI in Banking

What to coverExplain how you would integrate LLMs and RAG into a lending ecosystem to improve operational efficiency. Address the challenges of data privacy, model hallucination, and the orchestration of AI agents within a highly regulated financial environment.

Event-Driven Architecture for Loan Lifecycle Management

What to coverDescribe how you utilize Kafka or other messaging platforms to handle asynchronous events in a lending workflow. Discuss how you ensure data consistency and reliability across distributed systems when integrating with core banking and CRM platforms.

Ensuring Regulatory Compliance in Automated Lending

What to coverHow do you design technical solutions that satisfy strict credit policy, risk control, and regulatory reporting requirements? Explain your process for embedding compliance checks directly into the CI/CD pipeline and DevSecOps lifecycle.

Managing Scalability and Performance in Credit Origination

What to coverDetail your experience in designing high-throughput systems for credit assessment and decisioning. Discuss trade-offs between consistency and availability in distributed databases and how you optimize performance for mission-critical banking applications.

Source Code Review and Engineering Standards

What to coverAs a leader, how do you maintain high engineering standards while reviewing source code at a detailed level? Suggest how you balance the need for rapid delivery with the necessity of maintaining clean, maintainable, and secure codebases.

Integration Patterns for Enterprise Banking

What to coverDiscuss the challenges of integrating lending platforms with external credit bureaus and internal core banking systems. Explain your preferred enterprise integration patterns for ensuring secure, reliable, and scalable data exchange.

Technical Debt Management Strategy

What to coverHow do you prioritize technical debt against new feature development in a fast-paced banking environment? Provide a framework for identifying, quantifying, and communicating the business impact of technical debt to non-technical stakeholders.

Behavioural practice

Leading Through Change and Modernization

What to coverDescribe a situation where you led a team through a significant technological shift or platform modernization. Use the STAR method to explain how you managed resistance, maintained team morale, and ensured delivery excellence during the transition.

Cross-Functional Collaboration with Risk and Business Teams

What to coverProvide an example of how you successfully partnered with business, operations, or credit risk teams to define future-state capabilities. Highlight how you translated complex business requirements into actionable technical roadmaps.

Building a High-Performance Engineering Culture

What to coverShare an experience where you fostered a culture of accountability, innovation, and continuous learning within a large engineering team. Describe the specific actions you took to attract or retain talent and the resulting impact on team performance.

Navigating Complex Technical Decision-Making

What to coverDescribe a time you had to make a high-stakes technical decision regarding architecture or platform investment. Explain how you evaluated the trade-offs, engaged with stakeholders, and ensured the final decision aligned with long-term organizational goals.

02

Sharpen your knowledge

Topics to prepare

  • Lending Lifecycle Architecture

    Review the end-to-end credit origination, assessment, and loan servicing workflows. Exercise: Map a typical institutional lending journey from application to facility booking, identifying potential bottlenecks for automation. This is relevant to the role's requirement to lead platform strategy across the entire lending ecosystem.

  • Agentic AI in Banking

    Understand the conceptual difference between standard LLM implementations and agentic workflows that perform autonomous tasks. Exercise: Design a high-level architecture for an AI agent that automates credit document verification. This is relevant to the role's focus on driving GenAI and Agentic AI solutions for operational efficiency.

  • Banking Regulatory & Risk Controls

    Study the intersection of technology and banking compliance, specifically regarding data governance and security. Exercise: Outline a strategy for ensuring microservices-based lending platforms remain compliant with regulatory reporting and risk control standards. This is relevant to the role's mandate to ensure operational resilience and regulatory compliance.

Skills to revise

  • Java & Spring Boot Microservices

    Practice designing resilient, scalable microservices using Spring Boot. Exercise: Refactor a monolithic service into a set of decoupled microservices that communicate via events. This is relevant to the role's requirement for deep hands-on expertise in Java and microservices architecture.

  • Event-Driven Architecture with Kafka

    Focus on asynchronous communication patterns in enterprise systems. Exercise: Design a message-driven workflow for loan status updates using Kafka topics. This is relevant to the role's need for experience in event-driven architectures and enterprise integration patterns.

  • Cloud-Native Deployment (OpenShift)

    Gain proficiency in container orchestration and CI/CD pipelines. Exercise: Create a deployment strategy for a Spring Boot application on a container platform, including automated security scanning. This is relevant to the role's requirement for cloud-native modernization and DevSecOps practices.

04

Your preparation checklist

  • Map your lending domain expertise to the DBS ecosystem

    Prepare a summary of your experience in credit origination, loan servicing, or decisioning platforms. Be ready to discuss how you have managed the end-to-end lifecycle of lending systems, specifically focusing on integration with core banking, CRM, and regulatory reporting systems.

  • Develop a technical case study on AI-driven lending

    Prepare a walkthrough of a project where you implemented GenAI, RAG, or Agentic AI to improve operational efficiency or customer experience. Clearly explain the architecture, the choice of LLM integration, and how you ensured data governance and security in a regulated environment.

  • Refine your architectural approach for cloud-native modernisation

    Be prepared to discuss your strategy for migrating legacy lending platforms to cloud-native architectures. Focus on your experience with Java, Spring Boot, microservices, and event-driven patterns using Kafka, specifically addressing how you maintain resilience and manage technical debt.

  • Prepare evidence of engineering leadership and culture building

    Draft examples of how you have led high-performing engineering teams, managed technical roadmaps, and fostered a culture of continuous learning. Highlight your ability to balance hands-on code reviews with high-level strategic decision-making.

  • Formulate strategic questions for the hiring team

    Prepare questions regarding the current state of DBS's lending platform modernisation, the specific business priorities for AI integration in the Institutional Banking Group, and how the technology team balances rapid innovation with strict regulatory and risk compliance.

AI-generated from the job information · Prepared Oct 16, 2026 (UTC)Useful preparation, not a guarantee of eligibility or an offer.

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

KEEP EXPLORING

Similar AI jobs

Related skills and specializations in Singapore. Matched to this role, not your profile.

Explore more jobs
Finding similar opportunities…