New opportunity

Senior AI Solution Architect

Singtel Group · Singapore

About this role

The Senior AI Solution Architect will serve as a technical expert within the AIDA (Artificial Intelligence & Data Analytics) business unit at Singtel. This role is responsible for designing and deploying enterprise-grade, end-to-end AI solutions that are secure, scalable, and business-aligned. The architect will bridge the gap between AI vision and technical execution, collaborating with cross-functional teams across Networks and IT to integrate AI into existing infrastructures. Responsibilities include designing robust AI workflows, architecting the Central AI platform (including Agentic AI, RAG services, and model management), and ensuring compliance with enterprise policies and PDPA. The role also involves leading solutioning activities, managing a small development team, and evaluating emerging AI technologies. Required qualifications include a Bachelor’s or Master’s degree in a relevant field and 3 to 5 years of experience in solution architecture, AI/ML platform integration, and API-driven systems. Candidates must possess hands-on experience with cloud-based AI services (Azure ML, AWS SageMaker/Bedrock) and orchestration frameworks like LangChain, LangGraph, and Kubeflow. Strong communication skills are essential for bridging technical and business stakeholder requirements.

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

Skills for this role

Solution ArchitectureAI/ML Platform IntegrationData Pipeline DesignAPI-driven SystemsMicrosoft Azure MLAWS SageMakerAWS BedrockLangChainLangGraphGraphRAGRetrieval-Augmented Generation (RAG)RayKubeflowPortkeyCloud-native ArchitecturesModel OrchestrationAgentic AIVector Databases

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

Enterprise AI Platform Architecture
The role requires designing and delivering a Central AI platform that supports Agentic AI, RAG services, and model management. You must prioritize creating scalable, reusable design patterns that integrate seamlessly with existing IT and Network infrastructures while ensuring hybrid cloud interoperability.
Cross-Functional Stakeholder Alignment
You must act as the bridge between technical AI engineering teams and business stakeholders. This involves translating complex business requirements into technical blueprints and ensuring that AI solutions meet enterprise-wide goals for security, compliance, and PDPA adherence.
Technical Leadership and Team Management
Beyond individual architecture tasks, you are responsible for leading solutioning activities and managing a small development team. This requires providing technical guidance during prototyping, sprint planning, and the operationalization of AI models.

Likely challenges to think through

Integrating AI into Legacy Telco Infrastructure
Integrating modern, API-driven AI workflows with established, complex telecommunications systems presents a significant technical hurdle. Prepare by demonstrating how you have navigated hybrid environments and managed the trade-offs between adopting cutting-edge orchestration frameworks like LangGraph and maintaining system stability.
Balancing Innovation with Strict Governance
The need to rapidly evaluate and deploy emerging technologies like Agentic AI conflicts with the requirement for enterprise-grade security and PDPA compliance. Prepare by discussing your methodology for conducting risk-aware PoCs that satisfy both innovation goals and corporate governance standards.

Practice your answers

Technical practice

Designing Scalable RAG Architectures

What to coverExplain how you would architect a shared RAG service within an enterprise environment. Focus on the trade-offs between different vector database choices, retrieval strategies, and how to maintain data privacy and compliance with PDPA standards.

Orchestrating Agentic AI Workflows

What to coverDiscuss the architectural challenges of implementing agentic AI using frameworks like LangChain or LangGraph. Address how you manage state, handle tool-use reliability, and ensure these agents integrate securely with existing enterprise APIs.

Hybrid Cloud AI Deployment

What to coverDescribe your approach to deploying AI models across hybrid cloud and on-premise environments. Highlight how you ensure consistency in model serving, monitoring, and lifecycle management using tools like Kubeflow or Ray.

API-Driven AI Integration

What to coverHow do you design secure and scalable interfaces between LLM-based workflows and legacy IT or Network systems? Discuss the importance of API gateways, authentication, and rate limiting in an AI-first architecture.

Evaluating AI Tooling and Frameworks

What to coverWhen tasked with evaluating new AI platforms or tools like Portkey, what criteria do you prioritize? Suggestion: Focus on technical fit, interoperability with existing stacks, security, and long-term maintainability.

Model Lifecycle and Operations

What to coverExplain how you bridge the gap between AI engineering and production operations. Discuss how you implement model monitoring, versioning, and automated retraining pipelines to maintain performance over time.

Ensuring Security and Compliance in AI

What to coverHow do you embed governance and PDPA requirements into the design phase of an AI solution? Discuss the role of data masking, access controls, and auditability in enterprise-grade AI deployments.

Architecting for Telco-Scale Data

What to coverGiven the high-volume nature of telecommunications data, how do you design data ingestion pipelines that feed into AI models? Address the challenges of latency, data quality, and real-time processing requirements.

Behavioural practice

Bridging Technical and Business Stakeholders

What to coverDescribe a situation where you had to explain a complex AI architectural decision to non-technical stakeholders. How did you ensure they understood the business value and risks involved?

Leading a Cross-Functional Team

What to coverProvide an example of leading a small development team through an AI project. How did you manage technical disagreements and ensure the team remained aligned with the overall architectural vision?

Managing Technical Debt vs. Innovation

What to coverTell me about a time you had to balance the need for rapid prototyping with the requirement for long-term architectural stability. How did you decide which corners could be cut and which had to be robust?

Handling Project Ambiguity

What to coverDescribe a time you were tasked with a PoC for an emerging AI technology with limited documentation. How did you approach the evaluation process and communicate your findings to leadership?

Sharpen your knowledge

Topics to prepare

Enterprise AI Governance and Compliance
The role requires embedding PDPA and enterprise security policies into AI architecture. Exercise: Draft a high-level architectural design document for a RAG-based system that explicitly maps data flow, access controls, and PII masking techniques to meet strict regulatory compliance standards.
Agentic AI and Orchestration Patterns
The position involves architecting Agentic AI and using frameworks like LangGraph. Exercise: Design a conceptual workflow for an autonomous agent system that handles a multi-step business process, detailing how you would manage state, tool selection, and error handling between agents.
Hybrid Cloud AI Infrastructure
Singtel requires integration across hybrid cloud and on-prem environments. Exercise: Compare the architectural trade-offs of deploying model inference on AWS Bedrock versus a self-managed Kubeflow cluster, focusing on latency, data sovereignty, and operational overhead.

Skills to revise

RAG and Vector Database Integration
You must design shared RAG services. Exercise: Build a small prototype using a vector database and an orchestration framework to demonstrate how you would retrieve context-aware information to ground an LLM response, ensuring the pipeline is modular and scalable.
API-Driven System Design
The role emphasizes integrating AI models with enterprise IT and Network systems via APIs. Exercise: Define a RESTful API contract for an AI service that allows internal applications to trigger model inference, including authentication, rate limiting, and asynchronous job status tracking.
AI/ML Platform Orchestration
Hands-on experience with tools like Kubeflow and Ray is required. Exercise: Create a deployment blueprint for a model lifecycle that includes automated monitoring and retraining triggers, specifically identifying where you would integrate tools like Portkey for observability.

Your preparation checklist

Prepare an end-to-end AI architecture case study
Develop a presentation outlining a past project where you designed a scalable AI workflow. Focus on the integration of data pipelines, model orchestration, and API-driven systems within a hybrid cloud environment, specifically highlighting how you addressed security and compliance requirements.
Demonstrate expertise in agentic and RAG frameworks
Create a technical summary or code walkthrough demonstrating your experience with LangChain, LangGraph, and GraphRAG. Explain how you have utilized these tools to build context-aware agents and retrieval-augmented generation services for enterprise use cases.
Formulate a strategy for AI platform evaluation
Prepare a framework for how you evaluate emerging AI technologies and vendor tools (such as Portkey or Kubeflow). Be ready to discuss how you balance technical fit, scalability, and alignment with enterprise governance policies like PDPA.
Draft a communication plan for cross-functional stakeholders
Prepare examples of how you have translated complex AI technical blueprints into actionable insights for non-technical business leaders. Focus on your experience collaborating with IT and Network teams to integrate AI into existing infrastructure.
Develop targeted questions for the AIDA team
Prepare thoughtful questions regarding the current maturity of Singtel's Central AI platform, the specific challenges of integrating AI into telecommunications infrastructure, and how the AIDA unit balances rapid innovation with enterprise-grade security standards.

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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