New opportunity

Lead Data and AI Solution Engineer

Great Eastern · Singapore

About this role

Lead the operations of Great Eastern’s generative AI and agentic AI services at SG-GE Centre. The role focuses on efficient deployment, automation, governance, performance and cost optimization across Data & AI platforms.

Responsibilities

Lead deployment of GenAI and agentic AI pipelines; streamline operational processes and resource allocation.
Track and report incidents, coordinate resolution with relevant parties, and maintain communication with operational stakeholders.
Build automation to reduce manual deployment work. Develop data collection, operational dashboards and analytics to report performance metrics and insights.
Evaluate and implement tools and capabilities that expand automation coverage and increase deployment velocity. Design workflows and controls for governance and compliance, and lead operational-excellence initiatives.
Identify and mitigate business and regulatory compliance risks while keeping current with relevant industry trends, threats and technologies.

Qualifications

Bachelor’s degree in AI, Data Science, Computer Science, Engineering or a related field; a master’s degree is preferred.
At least 5 years of experience in Data & AI engineering or operations, focused on deploying and managing data engineering and machine learning pipelines. Hands-on exposure to operationalizing GenAI and agentic AI implementations is required.
Broad AI/ML, data engineering and analytics expertise; proficiency in a language such as Python, Java, Scala or R, plus strong SQL and Big Data querying skills.
Hands-on experience with GenAI or agentic AI frameworks such as LangChain, LangGraph, AgentCore and MCP; familiarity with frameworks such as TensorFlow, PyTorch and scikit-learn.
Understanding of SDLC, DevOps and Agile practices; ability to automate deployments and monitoring and develop operational dashboards. Strong communication, problem-solving, accountability and collaboration skills are expected.

Preferred

Cloud-platform familiarity and relevant AWS, GCP or Azure certifications are a plus. Financial-services or insurance experience is highly advantageous.

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

Skills for this role

Generative AIAgentic AIMLOpsData EngineeringMachine LearningAnalyticsPythonJavaScalaRSQLBig Data queryingLangChainLangGraphAgentCoreMCPTensorFlowPyTorchscikit-learnAWSGoogle Cloud PlatformMicrosoft AzureSDLCDevOpsAgileDeployment automationPerformance monitoringOperational dashboardsIncident managementGovernance and compliance

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

Lead production AI deployments
The role leads GenAI and agentic AI pipeline deployments across Data & AI platforms, drawing on experience managing data and machine-learning pipelines.
Increase automation and deployment velocity
The job calls for reducing manual deployment work, expanding automation coverage, and improving operational processes through new tools and methods.
Make operations measurable and responsive
Responsibilities include collecting performance data, developing operational analytics, tracking incidents, coordinating resolution, and communicating with stakeholders.
Balance governance, performance and cost
The role designs operational controls for compliance while improving resource allocation, key metrics, and the cost-effectiveness of GenAI and agentic AI services.

Likely challenges to think through

Speed versus operational controls
Preparation hypothesis: Faster automated releases may make governance harder to maintain. Prepare to explain how you would build checks and clear ownership into a deployment workflow without reverting to manual steps.
Resolving incidents across teams
Preparation hypothesis: Issues spanning AI pipelines and platforms may require several parties to coordinate under time pressure. Prepare an approach to triage, metric-based diagnosis, status updates, and follow-through.
Optimizing competing service metrics
Preparation hypothesis: Resource allocation may involve trade-offs between performance, deployment capacity, and cost. Prepare to describe which operational metrics you would compare and how you would evaluate a proposed improvement.

Practice your answers

Technical practice

How would you deploy and operate a GenAI pipeline across Data & AI platforms?

What to coverSuggested preparation: Walk through testing, release, monitoring and rollback, distinguishing model and data dependencies. Explain how you would balance deployment speed with reliability and governance.

What operational challenges differ between GenAI pipelines and agentic AI pipelines?

What to coverSuggested preparation: Discuss multi-step execution, tool interactions and failure handling alongside conventional pipeline concerns. Explain how experience with frameworks such as LangChain, LangGraph, agentcore or MCP would inform operational controls.

How would you automate deployments and monitoring to reduce manual work?

What to coverSuggested preparation: Describe where repeatable scripts and DevOps practices fit in the release lifecycle. Address validation, auditability and the risk of automating an unreliable process.

How would you diagnose and coordinate resolution of a production GenAI or agentic AI incident?

What to coverSuggested preparation: Explain how you would establish impact, use operational signals to narrow the cause, coordinate with relevant parties and verify recovery. Include incident tracking, reporting and stakeholder updates.

Which metrics would you collect for an operational dashboard, and how would you use them?

What to coverSuggested preparation: Propose measures for service performance, deployment outcomes, incidents and cost, noting that the posting does not prescribe specific metrics. Explain how data collection and analytics would turn those measures into actionable insights.

How would you optimize resource allocation and cost without undermining service performance?

What to coverSuggested preparation: Describe how you would identify demand and cost drivers, compare options and monitor the effect of a change. Make the trade-off between efficiency and reliable service explicit.

How would you strengthen governance and compliance in GenAI and agentic AI operations?

What to coverSuggested preparation: Describe workflows and controls that make deployments reviewable and operational risks visible. Explain how you would assess business and regulatory risks without assuming a particular regulation or company policy.

How would you use SQL and Big Data querying to investigate pipeline performance?

What to coverSuggested preparation: Outline how you would join operational and pipeline data, check data quality and isolate changes over time. Explain how the findings could guide monitoring or incident resolution.

How would you evaluate a new operations tool or capability?

What to coverSuggested preparation: Compare its potential effect on automation coverage and deployment velocity with integration effort, reliability and governance needs. Describe a limited evaluation and the evidence you would seek before wider adoption.

How do operational needs differ between conventional ML pipelines and GenAI workflows?

What to coverSuggested preparation: Connect data engineering and ML pipeline management to GenAI operationalization, including what must be monitored and validated after deployment. Draw on relevant familiarity with ML frameworks where useful, without assuming any one framework is used in this role.

Behavioural practice

Tell me about a time you coordinated several parties during an operational incident.

What to coverSuggested preparation: Use situation, action and result to show how you established priorities, assigned or clarified responsibilities, and kept stakeholders informed. Explain what the outcome taught you about incident handling.

Describe a time you explained a complex technical issue to a non-technical stakeholder.

What to coverSuggested preparation: Set out the situation, the explanation you chose and the resulting decision or understanding. Show how you communicated uncertainty and practical impact without losing technical accuracy.

Give an example of an operational improvement you led.

What to coverSuggested preparation: Describe the situation, your actions and the result, using a relevant measure such as reduced manual effort or improved deployment reliability if you have one. Explain how you checked that the change was sustainable.

Tell me about a time you identified and addressed a governance or compliance risk.

What to coverSuggested preparation: Use situation, action and result to show how you raised the concern, worked with appropriate parties and followed through. Focus on accountability without claiming knowledge of Great Eastern's internal controls.

Describe a time you adapted to a new technology or changing operational need.

What to coverSuggested preparation: Explain the situation, how you learned or tested an approach, and what resulted. Highlight collaboration and how you managed risk while making the change.

Sharpen your knowledge

Topics to prepare

GenAI and agentic AI operations
Suggested exercise: Map the stages of a GenAI or agentic AI pipeline from deployment through monitoring and issue resolution. The posting explicitly calls for leading deployments and operationalizing both types of pipeline.
SDLC, DevOps and Agile practices
Suggested exercise: Explain how a deployment change moves through development, review, release and monitoring, including where automation reduces manual work. The posting asks for strong understanding of these practices and deployment automation.
Performance, cost and resource optimization
Suggested exercise: Compare deployment time, performance and cost measures, then explain what an adverse trend might prompt you to investigate. Monitoring, cost optimization and resource allocation are stated responsibilities.
Governance and compliance risks
Suggested exercise: Sketch a workflow with approval, tracking and escalation controls, and explain how each could mitigate an operational or regulatory risk. The role includes designing controls and taking accountability for business and regulatory compliance risks.
Incident management and operational excellence
Suggested exercise: Walk through how you would track an incident, coordinate resolution, update stakeholders and identify a process improvement afterward. Each step reflects responsibilities in the posting.

Skills to revise

Automate a deployment task
Suggested exercise: Write a small Python script that validates deployment inputs and records the result, then identify the manual steps it replaces. The posting seeks programming proficiency and automation that reduces deployment effort; Python is one of several languages it names.
Work through an agent framework
Suggested exercise: Build a small illustrative workflow with LangGraph or LangChain and trace its execution and failure points. These are examples of frameworks named in the hands-on experience requirement, not mandated choices for a project.
Query operational data
Suggested exercise: Write SQL queries that aggregate sample pipeline runs by outcome and duration, and explain how you would adapt the analysis to Big Data querying tools. Strong SQL and Big Data querying skills are explicitly requested.
Create an operational dashboard
Suggested exercise: Turn sample deployment and incident data into a dashboard showing trends and exceptions, with a brief explanation of each measure. The role includes data collection, performance analytics and operational dashboards.
Communicate an incident update
Suggested exercise: Draft a concise status update covering impact, actions, responsible parties and the next update point; then restate it for a non-technical stakeholder. The posting emphasizes incident coordination, stakeholder communication and explaining technical concepts clearly.

Your preparation checklist

Map the role requirements to evidence
Prepare a one-page matrix with examples you can substantiate for Data & AI operations, pipeline deployment, GenAI and agentic AI operationalization, programming, SQL, and stakeholder communication. Note that the posting asks for 5+ years of relevant experience; do not treat preferred cloud certifications or insurance experience as mandatory.
Prepare a production pipeline walkthrough
Select one real project, if available, and diagram its deployment, monitoring, failure handling, and your contribution. Explain any GenAI or agentic AI framework you actually used; label a hypothetical example clearly if you lack a suitable project.
Practice an operations design exercise
Sketch a proposed deployment workflow for GenAI and agentic AI pipelines, including automation, checks, monitoring, rollback, and resource allocation. This is preparation for the posting’s deployment and operational-efficiency responsibilities, not a claimed Great Eastern architecture.
Build a sample metrics view
Prepare a small dashboard mock-up or SQL-based example showing how you would collect and report operational metrics. Include a rationale for measures such as performance, incidents, deployment velocity, and cost, which the posting identifies as operational priorities.
Rehearse an incident response
Write a short scenario showing how you would track an incident, coordinate resolution, and update technical and non-technical stakeholders. Prepare a concise example of explaining the impact and next steps without unnecessary technical detail.
Outline governance and risk controls
Draft a proposed control checklist for deployment approvals, monitoring, issue escalation, and review of business and regulatory compliance risks. Frame specific controls as your design suggestions; the posting does not prescribe a particular regulatory framework.
Prepare questions about the operating environment
Ask which Data & AI platforms and tools are in use, how deployment and incident responsibilities are divided, which metrics define operational excellence, and where governance controls need improvement. These questions address stated responsibilities without assuming Great Eastern’s current processes.

Availability last checked: 4 Nov 2026. Status can change after a check; confirm on the employer’s website. Report an issue · Our checking policy

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