#EG Senior / AI FinOps Engineer

NCS is a leading AI Tech Services company. With a 15,000-strong team across the Asia Pacific, NCS scales its platforms and capabilities to provide clients with greater agility and AI expertise across a range of Industries. Embracing a strong ecosystem of global partners, NCS transforms technology services delivery combining AI with digital resilience to drive real business impact. NCS is a subsidiary of the Singtel Group. As an AI FinOps Engineer , you will help NCS deliver cost-efficient, production-ready AI solutions by monitoring token usage and inference spend, optimising model selection, and putting cost governance in place across client engagements. You will build dashboards and cost reports, identify right-sizing opportunities across frontier and smaller models, support POC-to-production cost modelling, and work closely with AI Architects, LLMOps Engineers, Cloud Architects, and commercial teams to provide clear, defensible cost insights. At the Senior AI FinOps Engineer level, you will own FinOps standards across multiple AI engagements, guide pricing and commercial discussions with clients, mentor junior engineers, and help shape NCS-wide AI cost governance best practices. This role sits within NCS AI Central's (AIC) Forward Deployed Engineering (FDE) model — the combined capability that takes AI solutions from proof-of-concept through to hardened production systems. You will operate across both fast-moving FDE engagements (POC/POV, pilot deployments for strategic and lighthouse clients) and steady-state system development and maintenance work — bringing the same rigor and a reusable, asset-fed approach to both. What will you do? 1. Cost Monitoring & Optimisation • Build and maintain token spend and inference cost monitoring across engagements, with per-model, per-engagement, and per-client visibility. • Identify and act on right-sizing opportunities — routing between expensive frontier models and cheaper fine-tuned/smaller models without compromising quality. • Set up cost alerting and budget guardrails so engagements don't silently exceed projected AI spend. 2. Model Selection Economics • Provide cost-performance analysis to AI Architects and AI/LLM Specialists during model selection — translating benchmark results into a total-cost-of-ownership view. • Model the cost implications of scaling a POC/POV to production volume, so pricing and commercial conversations are grounded in real projected spend. 3. Governance & Reporting • Produce regular cost transparency reports for engagement leads and clients, supporting the PRR Model & Token Strategy pillar. • Maintain FinOps best-practice standards across AIIS engagements, aligned with broader NCS cloud FinOps practices. 4. FDE & Development/Maintenance Coverage • During FDE engagements: give fast, lightweight cost estimates during POC/POV to inform early client conversations about production viability. • During system development & maintenance engagements: own ongoing cost governance for live production AI systems, catching cost drift before it becomes a client issue. • Contribute reusable cost-modelling templates and dashboards back into the shared internal asset library. 5. Collaboration • Work closely with LLMOps Engineers (who own the monitoring infrastructure) and Cloud Architects on the underlying cost/performance tradeoffs. • Support commercial and presales conversations with defensible cost data, without owning the client proposal itself. Role Levels We Are Hiring For We are hiring at two levels for this role. All responsibilities above apply to both; the distinction is in scope of ownership, years of experience, and seniority of judgement expected. AI FinOps Engineer • 2–4 years of relevant experience. Builds and maintains cost dashboards, monitors spend, and executes model-cost analysis for individual engagements, under guidance from a Senior AI FinOps Engineer or AI Architect. • Supports cost reporting and right-sizing recommendations for one or two engagements at a time. Senior AI FinOps Engineer • 5+ years of relevant experience, including prior ownership of cloud/AI cost governance at scale. Owns the FinOps framework and standards across multiple engagements. • Advises directly in commercial/pricing conversations with clients, mentors junior FinOps and AI Engineers, and sets NCS-wide AI cost governance best practices. The ideal candidate should possess: • 2+ years in cloud FinOps, cost engineering, or a related technical-finance role, ideally with exposure to AI/ML workloads; 5+ years with end-to-end cost governance ownership expected at Senior level. • Solid understanding of LLM pricing models (token-based, per-request, reserved capacity) across major providers (OpenAI, Azure, AWS Bedrock, Vertex). • Comfortable working with cost/usage data — SQL, spreadsheet modelling, and basic scripting (Python) for automation and dashboards. • Able to translate technical usage data into clear, non-technical cost narratives for engagement leads and clients. • Comfortable working across both quick, directional cost estimates (FDE/POC) and rigorous, audited cost governance (production system maintenance). Preferred Qualifications • FinOps Foundation certification (FinOps Certified Practitioner) or equivalent cloud cost management experience. • Experience with cloud cost management tooling (CloudHealth, Kubecost, native AWS/Azure/GCP cost tools) extended to AI-specific cost tracking. • Exposure to Singapore Government commercial/procurement contexts. • Familiarity with model routing/gateway tools that expose per-request cost data (LiteLLM, Bedrock, Azure OpenAI). Tech Stack (Illustrative) • Languages: Python, SQL • Cost & Monitoring: Kubecost, cloud-native cost tools (AWS Cost Explorer, Azure Cost Management, GCP Billing) • LLM Cost Tracking: Model gateways/routers with per-request cost metering (LiteLLM, Bedrock, Azure OpenAI, Vertex) • Reporting: Excel/BI dashboards, Grafana for real-time cos…