NUS Enterprise
Higher Education
AIEngineeringLead,AutomationandPlatforms
“AI Engineering Lead, Automation and Platforms at NUS Enterprise. Skills: AI Engineering, Automation, Data Integration, Platform capabilities. Define technical architecture. Determine data lake connection”
What You'll Achieve.
Reduce manual administration; Reduce repetitive reconciliation; Reduce fragmented reporting
Industry & Context.
Translate business problems
What They're Looking For.
Must Have
Degree in Computer Science, 8+ years software engineering, 3+ years leading technical delivery, Hands-on Python experience, Hands-on API experience, Hands-on data pipelines experience, Hands-on deployment practices experience, Practical AI agents experience, Practical LLM workflows experience, Practical RAG experience, Practical embeddings experience, Practical vector databases experience, Practical knowledge retrieval experience, Practical AI inference layer experience, Experience with n8n, Experience with Airflow, Experience with Zapier, Experience with Make
Nice to Have
Experience with AI-assisted development tools, Experience with Google Workspace, Experience with Monday.com, Experience with Slack, Experience with Xero, Experience with Workable, Experience building internal automation platforms, Experience building knowledge search tools, Experience building dashboards, Experience building operational applications, Familiarity with local AI infrastructure, Familiarity with cloud-based AI infrastructure, Familiarity with data vectoring, Familiarity with RAG pipelines, Familiarity with knowledge retrieval systems, Experience in corporate services, Experience in operations, Experience in grants, Experience in finance, Experience in HR, Experience in administration
What You'll Do.
Define technical architecture
Determine data lake connection
Determine AI inference layer connection
Determine vector database connection
Determine workflow automation tools connection
Determine SaaS platforms connection
Determine ETP Hub connection
Translate business requirements
Translate operational pain points
Create technical solution designs
Create delivery plans
Prioritise build tasks
Ensure solutions are secure
Ensure solutions are maintainable
Ensure solutions are reusable
Deliver automation workflows
Deliver data pipelines
Deliver reporting automation
Deliver integration components
Review technical outputs
Review API integrations
Review AI-assisted tools
Review deployment approaches
Set delivery standards
Provide technical oversight
Guide AI inference layer development
Lead workflow automation approaches
Guide AI-enabled features
Ensure automation solutions integrate
Work with platform workstreams
Work with SaaS workstreams
Work with infrastructure workstreams
Align technical dependencies
Align implementation sequencing
Align support arrangements
Assess vendor proposals
Assess professional services proposals
Establish technical standards
Ensure documentation of architecture
Ensure documentation of data flows
Ensure documentation of business rules
Ensure documentation of workflow logic
Ensure documentation of prompts
Ensure documentation of APIs
Ensure documentation of dependencies
Ensure documentation of support arrangements
Put in place testing processes
Put in place monitoring processes
Put in place issue-resolution processes
Drive continuous improvement
How You'll Work.
Team & Collaboration
Cross-functional automation workstreams; Platform workstreams; SaaS workstreams; Infrastructure workstreams
Communication Scope
Explain technical trade-offs
Process & Methodology
Delivery discipline
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