AI Engineering Lead, Automation and Platforms

National University of Singapore; Singapore; On-site; SGD 120,000 - 180,000; Full time

Job summary

National University of Singapore is seeking a Technical Architecture and AI Engineering Leader to define architectures for AI automation, data integration and platform capabilities in a Singapore-based setting.

You will lead AI engineers to deliver AI agents, automation workflows, data pipelines and integrations, while setting delivery standards for code quality, testing and sustainment.

Qualifications

• Degree in CS or related technical discipline (or equivalent).
• 8+ years in software/AI/data engineering or digital solution delivery.
• At least 3 years leading technical delivery or cross-functional automation workstreams.
• Strong Python, APIs, data pipelines, system integration and deployment practices.
• Experience with AI agents, LLM workflows, embeddings, vector databases and AI inference design.
• Experience with workflow automation tools such as n8n, Airflow, Zapier, Make.
• Ability to translate business problems into scalable technical solutions.
• Strong communication and stakeholder management skills.

Responsibilities

• Define the technical architecture for AI automation, data integration and platform capabilities.
• Lead AI Engineers in delivering AI agents, automation workflows, data pipelines and integrations.
• Provide technical oversight for data cleanup, transformation and AI-enabled reporting.
• Assess vendor proposals for technical soundness, scalability and handover readiness.
• Establish standards for automation workflows, data pipelines, scripts and prompts.

Skills

Python

APIs

Data pipelines

System integration

Deployment practices

AI agents

LLM workflows

Stakeholder management

Education

Degree in Computer Science, Software Engineering, Data Science, Information Systems, Engineering or related discipline

Tools

n8n

Airflow

Zapier

Make

Google Workspace

Slack

Xero

Monday.com

GitHub Copilot

Job description

1. Technical Architecture and Solution Design

• Define the technical architecture for AI automation, workflow automation, data integration and platform capabilities across ETP.
• Determine how the data lake, AI inference layer, vector database, workflow automation tools, SaaSplatforms and ETP Hub should connect.
• Translate business requirements and operational pain points into technical solution designs, delivery plansand prioritised build tasks.
• Ensure technical solutions are secure, maintainable, reusable and aligned with ETP’s longer-term digital operating model.

2. AI Engineering Delivery Leadership

• Lead the AI Engineers in delivering AI agents, automation workflows, data pipelines, reporting automation and integration components.
• Review technical outputs including scripts, workflows, data logic, API integrations, AI-assisted tools and deployment approaches.
• Set delivery standards for code quality, testing, documentation, release readiness and post-launch sustainment.
• Support the use of AI coding tools to accelerate development while ensuring appropriate technical review and quality control.

3. Data, AI and Workflow Automation

• Provide technical oversight for data cleanup, data transformation, reconciliation logic, reporting automation and AI-enabled reporting.
• Guide the development of AI inference-layer capabilities for extraction, classification, validation,summarisation and report generation.
• Lead workflow automation approaches using tools such as n8n and related integration platforms.
• Guide AI-enabled features within ETP Hub, including search, knowledge retrieval, agent workflows and user-facing automation.

4. Platform, SaaS and Infrastructure Integration

• Ensure automation solutions integrate properly with ETP’s platforms, SaaS applications, workflow tools, datasources and infrastructure.
• Work with platform, SaaS and infrastructure workstreams to align technical dependencies, implementation sequencing and support arrangements.
• Assess vendor and professional services proposals for technical soundness, scalability, maintainability and handover readiness.
• Surface risks, constraints and trade-offs early, including security, data access, infrastructure, integration and sustainment considerations.

5. Governance, Documentation and Sustainment

• Establish technical standards for automation workflows, data pipelines, scripts, AI agents, prompts,integrations and reusable components.
• Ensure documentation of architecture, data flows, business rules, workflow logic, prompts, APIs, dependencies and support arrangements.
• Put in place testing, monitoring and issue-resolution processes for deployed automation and AI-enabled tools.
• Drive continuous improvement based on user feedback, adoption, operational performance and changing business needs.

Qualifications

• Degree in Computer Science, Software Engineering, Data Science, Information Systems, Engineering or arelated technical discipline; equivalent practical experience may be considered.
• Typically 8 or more years of relevant experience in software engineering, AI engineering, data engineering,platform integration or digital solution delivery.
• At least 3 years of experience leading technical delivery, solution architecture, engineering teams or cross-functional automation workstreams.
• Strong hands-on understanding of Python or similar languages, APIs, data pipelines, system integration anddeployment practices.
• Practical experience with AI agents, LLM workflows, RAG, embeddings, vector databases, knowledgeretrieval or AI inference-layer design.
• Experience with workflow automation tools such as n8n, Airflow, Zapier, Make or similar platforms.
• Ability to guide engineers, review technical work, challenge vendors, make architecture decisions andtranslate business problems into scalable technical solutions.
• Strong communication and stakeholder management skills, with the ability to explain technical trade-offsclearly to both technical and non-technical stakeholders.

Good to Have

• Experience with AI-assisted development tools such as Claude Code, Cursor, GitHub Copilot or similar tools.
• Experience with Google Workspace, Monday.com, Slack, Xero, Workable or similar SaaS applications.
• Experience building internal automation platforms, knowledge search tools, dashboards or operational applications.
• Familiarity with local or cloud-based AI infrastructure, data vectoring, RAG pipelines and knowledge retrieval systems.
• Experience in corporate services, operations, grants, finance, HR, administration or similar internal operating environments.