The Senior AI FinOps Engineer will join the AIDA (Artificial Intelligence & Data Analytics) team at Singtel to design and scale AI and data solutions. This role is responsible for owning the FinOps framework for the Central AI Kitchen, ensuring visibility and control over cloud, on-prem OpenShift, GPU infrastructure, and GenAI token consumption. Responsibilities include driving cost optimization for AI tokens and compute resources, establishing chargeback/showback models, and managing platform budgets and procurement. The engineer will partner with engineering, infrastructure, finance, and procurement teams to align AI investments with business value and financial efficiency. Key duties involve right-sizing resources, analyzing token consumption, managing cloud commitments, and building dashboards for monitoring spend and utilization. Required qualifications include a Bachelor’s degree in Computer Science, Engineering, or a related field and at least 6 years of experience in cloud infrastructure, platform engineering, FinOps, or technology cost management. Candidates must have hands-on experience with Kubernetes/OpenShift, cloud cost optimization, and financial modeling. Strong knowledge of GenAI/LLM cost drivers, including token usage and inference economics, is essential. The role requires excellent communication skills to bridge technical cost drivers with business-oriented language and a problem-solving mindset to balance performance with cost.
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A focused preparation guide, built around this job’s responsibilities and requirements.
✦ AI-generated guide
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01
Understand the role
What matters most
Establishment of Centralized FinOps Framework
The primary mandate is to design and maintain a comprehensive FinOps framework for the Central AI Kitchen. This requires integrating visibility across diverse environments including cloud, on-prem OpenShift, and GPU infrastructure, while implementing robust chargeback and showback models to ensure accurate cost attribution to specific projects and teams.
GenAI Token and Compute Optimization
You must drive cost efficiency for AI workloads by analyzing token consumption and infrastructure utilization. This involves balancing performance with cost through strategies such as model routing, prompt efficiency, resource right-sizing in OpenShift, and managing cloud commitments to ensure AI investments align with business value.
Cross-Functional Financial Governance
The role acts as a bridge between technical engineering teams and business functions like Finance and Procurement. You are responsible for administering budgets, forecasting spend, and managing procurement plans, requiring the ability to translate complex technical cost drivers into business-oriented language for management reporting.
Likely challenges to think through
Balancing Performance with Cost Constraints
A significant challenge is optimizing infrastructure and token usage without degrading application performance or reliability. Prepare by demonstrating how you have previously navigated trade-offs between aggressive cost-cutting measures and the technical requirements of high-performance AI models and GPU-intensive workloads.
Managing Hybrid Infrastructure Complexity
The role requires managing costs across both cloud and on-prem OpenShift environments simultaneously. Prepare by highlighting your experience with Kubernetes-native cost management tools like Kubecost or OpenCost and your ability to normalize cost data across disparate infrastructure types to provide a unified view of spend.
Driving Cultural Adoption of FinOps
Implementing governance and chargeback models often meets resistance from engineering teams focused on speed. Prepare by discussing your strategies for championing FinOps best practices and influencing stakeholders to adopt cost-conscious behaviors as a standard part of the AI development lifecycle.
03
Practice your answers
Technical practice
GenAI Token Cost Optimization Strategies
What to coverDiscuss methods to reduce inference costs, such as model routing, caching strategies, and prompt engineering for token efficiency. Explain how you would analyze cost-per-request to determine the most economical model for specific use cases.
Kubernetes and OpenShift Resource Management
What to coverExplain how you manage resource requests, limits, and quotas to prevent over-provisioning in a multi-tenant AI environment. Describe how you identify under-utilized GPU and CPU resources within an OpenShift cluster.
Implementing Chargeback and Showback Models
What to coverDescribe the process of establishing a cost allocation framework for shared AI infrastructure. Address how you handle tagging strategies and attribution for costs across different projects, teams, and applications.
Cloud and On-Premise Cost Monitoring
What to coverExplain how you would use tools like Kubecost, OpenCost, or Grafana to build dashboards for tracking spend and utilization. Discuss how you set up alerts for budget anomalies and cost spikes.
GPU Infrastructure Economics
What to coverDiscuss the specific cost drivers associated with GPU-intensive workloads in an AI environment. Explain how you balance the need for high-performance compute with financial efficiency and procurement commitments.
Technology Budgeting and Forecasting
What to coverOutline your approach to managing infrastructure budgets, including demand forecasting and variance analysis. Suggestion: Prepare to discuss how you align technical capacity requirements with financial procurement cycles.
Balancing Performance and Cost
What to coverExplain your methodology for ensuring that cost-optimization measures do not negatively impact application reliability or performance. Provide examples of how you evaluate trade-offs between model latency and inference cost.
Behavioural practice
Bridging Technical and Business Perspectives
What to coverDescribe a situation where you had to explain complex technical cost drivers to non-technical stakeholders like Finance or Procurement. Focus on how you translated technical metrics into business-oriented language to influence decision-making.
Influencing Cross-Functional Teams
What to coverProvide an example of how you partnered with engineering or infrastructure teams to implement a difficult cost-saving initiative. Detail the steps you took to gain buy-in and overcome resistance to changing existing workflows.
Managing Budgetary Risks
What to coverShare an experience where you identified a significant budget risk or cost anomaly. Explain the actions you took to mitigate the risk and the resulting impact on the project or platform budget.
Problem-Solving in Resource Constraints
What to coverDescribe a time you had to innovate to balance performance requirements with strict financial constraints. Detail the situation, the specific actions you took to optimize resources, and the measurable outcome.
02
Sharpen your knowledge
Topics to prepare
GenAI Inference Economics
Study the cost drivers of LLM deployments, specifically the impact of input/output token ratios, context window size, and model routing strategies. Exercise: Create a theoretical cost model comparing the inference cost per 1,000 tokens across different model sizes and deployment regions to demonstrate how model selection affects unit economics.
FinOps Frameworks for AI Platforms
Understand the lifecycle of cloud and on-prem AI infrastructure costs, focusing on chargeback/showback models and governance. Exercise: Draft a proposal for a tagging and cost-allocation strategy that distinguishes between shared GPU cluster costs and project-specific token consumption in a multi-tenant environment.
Kubernetes Resource Management
Deepen knowledge of Kubernetes/OpenShift resource requests, limits, and quotas as they relate to GPU and compute utilization. Exercise: Analyze a hypothetical scenario where GPU utilization is low despite high cluster costs; identify how to adjust pod scheduling and resource requests to improve efficiency without impacting model latency.
Skills to revise
Kubecost and OpenCost Implementation
Gain proficiency in using Kubernetes-native cost monitoring tools to track spend. Exercise: Configure a mock dashboard in a local Kubernetes cluster using OpenCost to visualize namespace-level resource consumption and identify idle capacity.
Grafana Dashboarding for Financial Metrics
Develop skills in creating observability dashboards that translate technical utilization data into financial insights. Exercise: Build a Grafana dashboard that correlates GPU utilization metrics with daily token consumption costs to track cost-per-request trends over time.
Technology Budgeting and Forecasting
Practice the administrative side of FinOps, including variance analysis and procurement planning. Exercise: Create a spreadsheet-based forecast model that projects annual cloud and GPU infrastructure spend based on anticipated growth in AI model inference traffic.
04
Your preparation checklist
Develop a GenAI Cost Optimization Framework
Prepare a structured approach to managing LLM inference costs. Outline how you would analyze token consumption, implement caching strategies, and evaluate model routing to balance performance with cost-per-request.
Design a Kubernetes/OpenShift Resource Allocation Model
Create a technical summary of how you would manage GPU and compute utilization in an OpenShift environment. Include your strategy for setting resource requests, limits, and quotas to prevent under-utilization.
Prepare a Chargeback/Showback Strategy
Draft a proposal for an internal cost-allocation model. Explain how you would tag resources and report usage metrics to ensure different business units are held accountable for their specific AI infrastructure consumption.
Build a Sample FinOps Dashboard Concept
Sketch a dashboard layout using tools like Grafana that tracks key metrics such as GPU utilization, GenAI token spend, and budget variance. Be prepared to explain how you would set up alerts for cost anomalies.
Formulate Strategic Questions for the AIDA Team
Prepare questions regarding the current maturity of their FinOps framework, the primary challenges in their GPU infrastructure procurement, and how they currently bridge the gap between technical infrastructure costs and business-level financial reporting.
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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