AI Engineering Lead - Automation and Platforms
No salary listed
Senior, Expert
Bachelor's
Singapore
In PersonOn-site at Kent Ridge Campus in Singapore.
Engineering ManagementEngineering Leadership
About the job
Requirements
- A degree in Computer Science, Software Engineering, Data Science, Information Systems, Engineering, or a related 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, application programming interfaces, data pipelines, system integration, and deployment practices.
- Practical experience with AI agents, large language model workflows, retrieval-augmented generation, embeddings, vector databases, knowledge retrieval, 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, and translate business problems into scalable technical solutions.
- Strong communication and stakeholder management skills, with the ability to explain technical trade-offs clearly to technical and non-technical stakeholders.
Responsibilities
- Define 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, SaaS platforms, and ETP Hub should connect.
- Translate business requirements and operational pain points into technical solution designs, delivery plans, and prioritised build tasks.
- Ensure technical solutions are secure, maintainable, reusable, and aligned with ETP’s longer-term digital operating model.
- Lead AI Engineers in delivering AI agents, automation workflows, data pipelines, reporting automation, and integration components.
- Review technical outputs, including scripts, workflows, data logic, application programming interface 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.
- Provide technical oversight for data cleanup, data transformation, reconciliation logic, reporting automation, and AI-enabled reporting.
- Guide 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.
- Ensure automation solutions integrate properly with ETP’s platforms, SaaS applications, workflow tools, data sources, 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.
- 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, application programming interfaces, dependencies, and support arrangements.
- Put testing, monitoring, and issue-resolution processes in place for deployed automation and AI-enabled tools.
- Drive continuous improvement based on user feedback, adoption, operational performance, and changing business needs.
Desired Qualifications
- 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, retrieval-augmented generation pipelines, and knowledge retrieval systems.
- Experience in corporate services, operations, grants, finance, human resources, administration, or similar internal operating environments.
About the company
NUS Enterprise supports and grows entrepreneurship at the National University of Singapore. It seeds ideas, sparks innovations, and scales ventures from campus into global companies by offering experiential learning, ecosystem building, technology translation and commercialisation, and strategic global partnerships. It has helped nearly 4,300 students and over 3,000 start-ups, including 10 unicorns, and aims to improve the lives of 1 billion people in the next decade by building a worldwide innovation network. Their approach combines education, mentorship, access to resources and markets, and collaborations to turn research and ideas into real-world impact.
Company Size
N/A
Company Stage
N/A
Total Funding
$2.9M
Headquarters
Singapore, Singapore
Founded
2001
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Simplify's Take
What believers are saying
- June 2026 brought Playground Global and Matter Venture Partners into the US$117 million NUS VC programme.
- The Studio in Silicon Valley gives founders labs, prototyping, and direct market validation.
- UnternehmerTUM partnership opens European customers, investors, and talent for NUS deep-tech startups.
What critics are saying
- Nova cannot fix NUS’s unlicensed IP glut; 70%-80% of university patents stay unlicensed.
- Shanghai and Munich outposts demand local dealflow; thin execution turns expansion into overhead.
- If NUS ventures never produce a $100 million company by 2035, NUSX becomes branding theater.
What makes NUS Enterprise unique
- Sept. 24, 2026 rebrand to NUSX unifies venture building and IP commercialization.
- Nova scans 20,000 patents and 9,000 projects, compressing commercialization from weeks to minutes.
- NUSX now spans Silicon Valley, Munich, and Shanghai across research, capital, and talent.
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