New opportunity

Assistant Manager, Data & AI

Sentosa Development Corporation · Singapore

About this role

The Assistant Manager, Data & AI is responsible for the hands-on engineering, deployment, and operational health of Sentosa Development Corporation's (SDC) Data & AI applications and infrastructure. The role focuses on building and deploying AI products for guest and staff use cases, automating data pipelines, and managing security governance. Key Responsibilities: AI Application Development: Execute roadmaps for conversational assistants, RAG systems, and decision support tools; lead development from prototype to production; contribute to agentic systems. Data Infrastructure: Design and maintain enterprise data pipelines; automate ingestion processes; manage cloud data warehouses and analytics environments. Security & Governance: Serve as technical liaison for WOG security assessments and VAPT; ensure compliance with IM8 and government cloud standards. Stakeholder Engagement: Translate business requirements into deliverables and manage external vendors. Qualifications: Master's or Bachelor's degree in Computer Science, Engineering, Data Science, or related field. Minimum 3-5 years of progressive experience in data/software engineering or AI development. Required skills include cloud-based data pipeline operation, enterprise data warehouse familiarity (e.g., Snowflake, MSSQL), and strong technical depth in SQL/NoSQL and ETL design. Preferred qualifications include experience with WOG digital policies, government commercial cloud environments, and IM8 requirements. Candidates should possess practical experience in cloud-native development, CI/CD, containerisation, and infrastructure-as-code, alongside a sound understanding of IT security principles.

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

Skills for this role

Data EngineeringSoftware EngineeringAI Application DevelopmentSQLNoSQLETL/ELTData OrchestrationFull Stack DevelopmentLLMsRetrieval-Augmented GenerationVector DatabasesPrompt EngineeringCloud-native developmentCI/CDContainerisationInfrastructure-as-codeSnowflakeMSSQLVulnerability Management

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 AI Engineering
The role requires hands-on development of AI products, specifically RAG systems and conversational assistants, from prototype to production. You must demonstrate the ability to build modular, scalable AI applications while managing the full lifecycle of these tools.
Data Infrastructure Reliability
You are responsible for designing and maintaining enterprise data pipelines and cloud data warehouses. A key priority is automating manual ingestion processes to ensure data freshness, accuracy, and cost-effective performance tuning.
WOG Security and Governance Compliance
As a technical liaison, you must ensure all engineering work adheres to IM8 standards and WOG digital policies. This includes managing vulnerability assessments, penetration testing (VAPT), and maintaining a rigorous security posture for all AI infrastructure.
Vendor and Stakeholder Management
You must translate complex business requirements into technical deliverables while managing external vendors. Success depends on your ability to oversee project milestones, ensure technical quality, and provide post-delivery support.

Likely challenges to think through

Balancing Innovation with Strict Governance
The challenge lies in deploying modern AI technologies like agentic systems while remaining strictly compliant with IM8 and government cloud standards. Prepare by reviewing your experience in navigating rigid security frameworks without stifling technical progress.
Managing Parallel Technical Workstreams
The role involves simultaneous responsibilities in AI development, data pipeline maintenance, and security liaison duties. Prepare by highlighting your ability to prioritize tasks, manage time across diverse projects, and maintain focus on both long-term roadmaps and immediate operational health.
Vendor Oversight and Quality Assurance
You will be responsible for the technical output of external vendors. The challenge is ensuring their work meets SDC's high standards for reliability and security. Prepare by discussing your approach to defining clear technical requirements and enforcing contractual quality benchmarks.

Practice your answers

Technical practice

Designing RAG Systems

What to coverExplain the architecture of a Retrieval-Augmented Generation system. Discuss the trade-offs between different vector database choices and how you ensure the relevance and accuracy of retrieved context for LLM generation.

Data Pipeline Automation and Orchestration

What to coverDescribe your approach to building scalable ETL/ELT pipelines. Suggestion: Focus on how you handle data freshness, error handling, and the use of modern orchestration tools to automate manual ingestion processes.

Cloud-Native Security and Governance

What to coverHow do you integrate security into the CI/CD lifecycle for AI applications? Discuss your experience with vulnerability management and ensuring compliance with strict standards like IM8 or similar government frameworks.

Managing Enterprise Data Warehouses

What to coverCompare the operational differences between Snowflake and MSSQL in a cloud environment. Discuss strategies for performance tuning, cost optimization, and maintaining data integrity in a central data platform.

Infrastructure-as-Code (IaC) and Containerisation

What to coverExplain the benefits of using IaC for deploying AI infrastructure. Suggestion: Describe how you use containerisation to ensure consistency across development, staging, and production environments.

Agentic Systems Development

What to coverWhat are the unique engineering challenges when moving from simple conversational assistants to agentic systems? Discuss modular design, state management, and how you handle tool-use capabilities within an AI application.

SQL and NoSQL Trade-offs

What to coverIn what scenarios would you choose a NoSQL database over a traditional SQL database for an AI-driven project? Provide examples based on data structure, scalability requirements, and query patterns.

Vulnerability Assessment and Penetration Testing (VAPT)

What to coverDescribe your role in preparing systems for VAPT. Suggestion: Explain how you remediate security findings and maintain a documented security posture for production-grade AI tools.

Behavioural practice

Translating Business Requirements to Technical Deliverables

What to coverDescribe a time you had to translate complex business needs into a concrete engineering roadmap. How did you manage stakeholder expectations regarding scope and project timelines?

Managing External Vendors

What to coverGive an example of how you managed a technical vendor to ensure project milestones and quality standards were met. How did you handle situations where the vendor's output did not align with contractual commitments?

Handling Conflicting Priorities

What to coverThis role requires working across multiple parallel workstreams. Describe a situation where you had to prioritize competing tasks and how you ensured all deliverables remained on track.

Proactive Learning and Adaptation

What to coverThe AI field evolves rapidly. Provide an example of a new technology or framework you recently learned and how you applied it to improve an existing process or product.

Sharpen your knowledge

Topics to prepare

WOG Security and IM8 Governance
The role requires acting as a technical liaison for government security assessments and compliance with IM8 standards. Exercise: Review the public-facing IM8 guidelines and map how standard data pipeline security controls (encryption at rest/transit, IAM, logging) align with these government requirements.
RAG and Agentic AI Architecture
The position involves building conversational assistants and agentic systems. Exercise: Design a conceptual architecture for a RAG system that includes a vector database, an LLM, and an orchestration layer, specifically identifying where security and data governance hooks would be placed.

Skills to revise

Cloud-Native Data Pipeline Automation
The role requires designing and maintaining enterprise pipelines with a focus on automation. Exercise: Create a sample CI/CD pipeline configuration (e.g., using GitHub Actions or GitLab CI) that automates the testing and deployment of an ETL script to a cloud data warehouse like Snowflake.
Vulnerability Management and VAPT
The candidate must manage VAPT processes and remediate findings. Exercise: Simulate a vulnerability assessment report for a web application and draft a remediation plan that prioritizes findings based on severity and business impact, consistent with standard security review cycles.
Infrastructure-as-Code (IaC)
Practical experience with IaC is required for managing cloud environments. Exercise: Write a basic Terraform or CloudFormation script to provision a secure, private cloud storage bucket or database instance, ensuring it includes necessary tags and encryption settings.

Your preparation checklist

Prepare a technical project walkthrough
Select a past project where you built a production-grade data pipeline or AI application. Prepare to discuss your specific role in the architecture, the choice of tools (e.g., SQL/NoSQL, orchestration), and how you handled data quality or scalability challenges.
Review WOG and IM8 compliance standards
Familiarize yourself with the core principles of Singapore's IM8 (Instruction Manual 8) and WOG (Whole-of-Government) security governance. Be ready to explain how you have previously integrated security, vulnerability management, or VAPT processes into your development lifecycle.
Document your AI engineering experience
Create a summary of your hands-on experience with LLMs, RAG systems, and vector databases. Be prepared to explain your approach to prompt engineering and how you manage the transition of AI tools from prototype to production environments.
Outline your vendor and stakeholder management approach
Prepare examples of how you have translated business requirements into technical deliverables and managed external vendors to meet project milestones, quality standards, and contractual obligations.
Formulate strategic questions for the interviewers
Prepare thoughtful questions regarding the current AI roadmap at SDC, the specific cloud-native stack used for their data platforms, and how the team balances rapid AI innovation with strict government security requirements.

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…