New opportunity

Vice President, Full-Stack Engineer (APAC AI Hub)

BNY · Singapore

About this role

BNY seeks a Vice President, Full-Stack Engineer for its APAC AI Hub in Singapore. This hands-on role builds and supports secure, scalable AI-powered products and reusable enterprise platform capabilities, working with regional and global engineering, product, and control partners.

Responsibilities

Design, develop, test, deploy, and support AI applications, services, backend APIs, integrations, context pipelines, automation, and platform components across the software development lifecycle.
Apply LLMs, retrieval-augmented generation, agentic workflows, model APIs, and prompt engineering. Evaluate model and solution quality, then improve models, prompts, workflows, and application designs.
Translate stakeholder requirements into technical solutions; write testable code, conduct code reviews, document designs, build automated tests and deployment pipelines, and resolve production issues.
Follow security, privacy, model-risk, responsible-AI, change-management, and software-quality standards. Improve reliability and reuse, manage technical debt, and mentor less-experienced engineers.

Qualifications

A bachelor’s degree in Computer Science, Engineering, Information Systems, or a related discipline, or equivalent professional experience, is required. Experience in software engineering, applied AI/ML, platform development, or a related discipline is sought, preferably 7+ years.
Production experience with applications, backend services, Java Spring Boot, APIs, integrations, or cloud platforms; practical experience with LLM applications, RAG, prompt engineering, session context management, model APIs, AI agents, evaluation, or related technologies.
Strong programming skills, preferably Python and Java, with modern backend or cloud-native frameworks. Knowledge of architecture, testing, CI/CD, source control, containers, monitoring, security, and production support; strong troubleshooting, collaboration, and communication skills. Banking, financial-services, or other regulated-environment experience is a plus.

The position has a full-time day schedule. BNY describes competitive compensation, benefits, wellbeing programs, flexible resources, and paid leave, including volunteer time; no salary figure is stated. The listed application deadline is October 14, 2026, at 9:30 PM.

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

Skills for this role

Full-stack developmentSoftware engineeringPythonJavaSpring BootLarge language modelsRetrieval-augmented generationAI agentsPrompt engineeringModel APIsSession context managementModel evaluationBackend developmentAPI developmentCloud platformsSoftware architectureAutomated testingCI/CDSource controlContainersMonitoringSecurityProduction supportTechnical documentationCollaborationCommunication

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

Deliver and support production AI services
The role covers design, development, testing, deployment, and production support of AI applications and services, with emphasis on reliability and operational readiness.
Evaluate and improve applied AI solutions
BNY calls for practical use of LLMs, retrieval-augmented generation, agentic workflows, model APIs, and prompts, followed by evaluation of quality and performance and improvements to the solution.
Build reusable backend platform capabilities
Backend services, APIs, integrations, context pipelines, and automation support the enterprise AI platform. The posting specifically mentions production Java Spring Boot services and prefers strong Python and Java skills.
Engineer within enterprise controls
Security, privacy, model risk, responsible AI, change management, and software quality standards must be applied throughout delivery, not added only at release.
Lead through collaboration and engineering practice
The engineer translates requirements with product, architecture, governance, and business partners; reviews code, documents designs, manages technical debt, and mentors less-experienced engineers across the APAC AI Hub.

Likely challenges to think through

Balancing AI quality with dependable operation
A likely challenge, not a confirmed BNY problem, is improving variable LLM outputs while keeping services testable and supportable. Prepare a concrete example of how evaluation results informed prompt or workflow changes, automated tests, monitoring, and production troubleshooting.
Moving quickly without bypassing controls
Applied AI delivery may require trade-offs between iteration speed and security, privacy, model-risk, and change-management standards. Prepare to explain how you would surface control requirements early and document decisions while still delivering usable increments.
Meeting local needs without fragmenting the platform
Working with regional and global partners may make a tailored solution faster initially than a reusable component. Prepare an example of how you would clarify differing requirements, decide what belongs in a shared API or platform capability, and manage the resulting technical debt.

Practice your answers

Technical practice

How would you design a secure, reusable AI application for enterprise use?

What to coverThe posting covers AI applications, services and reusable platform components. Suggested preparation: explain service boundaries, integration points, scalability and maintainability trade-offs, and how you would make the design testable.

How would you build and improve a retrieval-augmented generation pipeline?

What to coverThe role explicitly includes retrieval-augmented generation and context pipelines. Suggested preparation: describe how you would select and supply relevant context, then assess whether retrieval quality or generation quality is causing poor answers.

When would you use an agentic workflow rather than a simpler LLM workflow?

What to coverThe posting names agentic workflows, model APIs and prompt engineering. Suggested preparation: compare the flexibility of multi-step actions with added complexity, latency and control needs.

How would you manage session context in an LLM application?

What to coverSession context management is listed among the relevant AI technologies. Suggested preparation: discuss what context to retain, how to limit irrelevant or sensitive information, and how you would test continuity across interactions.

How would you evaluate an AI service and decide what to improve?

What to coverThe role calls for model and solution evaluations followed by improvements to models, prompts, workflows and application designs. Suggested preparation: explain how you would define representative tests, examine quality and performance results, and distinguish a prompt issue from a wider design issue.

How would you design and support a production backend API for an AI capability?

What to coverThe posting emphasizes production backend services, APIs and integrations, and specifically mentions Java Spring Boot alongside Python and modern backend frameworks. Suggested preparation: discuss interface design, error handling, integration testing and maintainable code without assuming a particular system architecture.

How would you test and deploy changes to an AI service?

What to coverAutomated testing, deployment pipelines, CI/CD, source control and containers are all named in the posting. Suggested preparation: distinguish tests for deterministic service behavior from checks on variable model outputs, and explain how you would reduce deployment risk.

How would you diagnose a production AI service that has become slower or less reliable?

What to coverThe role includes monitoring, troubleshooting production issues and improving operational readiness. Suggested preparation: describe how you would use observable symptoms to narrow down an API, integration, context-pipeline or model-call problem, then verify the fix.

How would you apply security, privacy and model-risk controls during delivery?

What to coverThe posting explicitly requires security, privacy, model-risk, responsible-AI and change-management standards. Suggested preparation: explain how you would identify risks early, document design decisions and validate controls without claiming knowledge of BNY's specific internal procedures.

How would you decide whether to build a reusable platform component or a product-specific solution?

What to coverThe role calls for reusable capabilities, technical-debt management and continuous improvement. Suggested preparation: weigh current requirements against reuse potential, maintenance cost and the risk of abstracting too early.

Behavioural practice

Tell me about a time you turned ambiguous stakeholder requirements into an engineering solution.

What to coverThe role involves working with product, architecture, design, governance and business partners. Suggested preparation: use a real situation, your actions to clarify requirements and trade-offs, and the resulting decision or outcome.

Tell me about a time you resolved a difficult production issue.

What to coverProduction support and troubleshooting are stated responsibilities. Suggested preparation: describe the situation, how you investigated and communicated during the issue, and what changed as a result.

Tell me about a time you coordinated engineering work across teams or locations.

What to coverThe posting highlights collaboration with regional and global partners. Suggested preparation: give a real example of the coordination challenge, your actions to align people, and the outcome.

Tell me about a time you raised a security, privacy or quality concern during delivery.

What to coverThe role requires applying enterprise controls while delivering software. Suggested preparation: explain the situation, how you surfaced and addressed the concern with relevant partners, and the result.

Tell me about a time you helped a less-experienced engineer improve their work.

What to coverMentoring and contributing to shared technical practices are stated responsibilities. Suggested preparation: describe the engineer's situation, your coaching or review actions, and the observable result without overstating your contribution.

Sharpen your knowledge

Topics to prepare

Enterprise AI architecture and reuse
The posting calls for secure, scalable AI products and reusable platform components. Revision exercise (suggestion): sketch an application architecture showing backend APIs, integrations, context pipelines, and reusable components; explain where you would address maintainability and technical debt.
LLM application patterns
The role names retrieval-augmented generation, agentic workflows, prompt engineering, model APIs, and session context management. Revision exercise (suggestion): compare when a direct model call, retrieval, or an agentic workflow would be appropriate, and trace how session context would pass through each.
Model and solution evaluation
The posting requires evaluating performance and quality and recommending improvements. Revision exercise (suggestion): define a small set of test questions and a quality rubric, then explain how the results would guide changes to prompts, retrieval, workflows, or application design.
Enterprise controls for AI delivery
Security, privacy, model risk, responsible AI, change management, and software quality are explicit delivery standards. Revision exercise (suggestion): review a hypothetical AI-service design and identify control questions to resolve before deployment.
Production readiness across the lifecycle
The role covers deployment, monitoring, reliability, and production support. Revision exercise (suggestion): outline what you would check before release and how you would distinguish an API defect, integration failure, and model-quality issue after release.

Skills to revise

Build backend APIs
The posting emphasizes production backend services, APIs, integrations, Python, Java, and specifically Java Spring Boot. Revision exercise (suggestion): implement a small, testable API in Java Spring Boot or Python, with input validation, an integration boundary, and documented design choices.
Implement an LLM workflow
Practical LLM applications, retrieval-augmented generation, model APIs, prompts, and session context are named. Revision exercise (suggestion): prototype a small retrieval-backed request flow using a model API available to you, then inspect how prompt and context changes affect its responses.
Automate testing and delivery
The posting calls for automated tests and deployment pipelines, alongside knowledge of CI/CD, source control, and containers. Revision exercise (suggestion): add unit and API tests to a small service and configure a pipeline to run them; explain how you would prepare the service for deployment.
Troubleshoot a production issue
Monitoring, defect resolution, and production support are explicit. Revision exercise (suggestion): use logs and monitoring data from a sample service to trace a failed request, isolate the likely cause, and write a short fix-and-verification note.
Turn requirements into a reviewed design
The role works with product, architecture, governance, and business partners; it also includes documentation, code reviews, and mentoring. Revision exercise (suggestion): turn a sample stakeholder request into a brief technical design, identify open control questions, and practice explaining review feedback to another engineer.

Your preparation checklist

Prepare a production-system walkthrough
The role calls for production applications, backend services, APIs, integrations, and cloud platforms. If you have a relevant example, prepare a diagram and a concise account of your design decisions, testing, deployment, support, and outcomes.
Map your technical evidence to the role
Prepare brief examples of Python, Java or Spring Boot, API development, and cloud-native work where applicable. Distinguish hands-on contributions from team results; the posting prefers these skills rather than requiring every technology.
Practice an enterprise AI design exercise
The role covers LLMs, RAG, agents, model APIs, and session context. As preparation, sketch an AI service with an API, retrieval and context flow, failure handling, and reusable components; explain your trade-offs.
Build an evaluation plan
The posting calls for model and solution evaluation. Prepare a sample test set, quality and performance measures, and a way to compare prompts or workflows and decide what to improve.
Explain delivery and production readiness
Prepare an example showing automated tests, code review, CI/CD, monitoring, and troubleshooting. Be ready to explain how you documented a design, handled an incident, or reduced technical debt, if applicable.
Rehearse a controls-and-collaboration discussion
The role involves security, privacy, model risk, responsible AI, and change management, alongside regional and global partners. Prepare a scenario explaining how you would surface risks, agree on requirements, and communicate decisions; include a mentoring example if you have one.
Write targeted questions for the interviewers
Ask how the APAC AI Hub divides ownership of shared platform components, evaluates AI quality, coordinates with control partners, and supports services in production. These are questions to clarify, not assumptions about BNY’s processes.

Availability last checked: 4 Nov 2026. Status can change after a check; confirm on the employer’s website. Report an issue · Our checking policy

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