Senior AI FinOps Engineer

The Senior AI FinOps Engineer will own the FinOps framework for our client’s AI platform, providing visibility and control across cloud, on-premises OpenShift, GPU infrastructure and GenAI token consumption. The role will drive sustainable unit economics while maintaining application performance and supporting informed investment, capacity and sourcing decisions.

Role Overview

This role sits across platform engineering, ML/LLMOps, IT infrastructure, finance and procurement teams. Success will involve improving cost attribution, utilisation and efficiency across AI workloads while establishing consistent FinOps practices and reporting for projects, teams and applications.

Key Skills & Experience

• Bachelor’s degree in Computer Science, Engineering or a related field


• Hands-on experience managing and optimising cloud infrastructure costs, including budgeting, forecasting, cost allocation and commercial commitments


• Strong knowledge of cloud FinOps practices, including budgeting, forecasting, cost allocation, chargeback/showback, anomaly management and unit economics


• Experience working with Kubernetes or OpenShift environments and analysing infrastructure resource utilisation and cost


• Working knowledge of Kubernetes or OpenShift resource management, including CPU, memory, storage and GPU utilisation, requests and limits, quotas and workload scheduling


• Knowledge of Kubernetes or OpenShift cost management tools such as Kubecost or OpenCost


• Knowledge of observability platforms such as Grafana


• Understanding of GenAI and LLM cost drivers, including input and output tokens, model serving, GPU utilisation, context size, model routing and inference economics


• Ability to analyse AI token consumption and identify optimisation opportunities across prompts, models, applications and LLM endpoints


• Experience developing dashboards and management reports using Grafana or equivalent tools


• Experience in technology budgeting, procurement analysis, vendor quotations and commercial or cost modelling


• Experience partnering with finance, procurement and engineering teams on technology budgets, procurement and cost optimisation


• Strong communication skills to explain technical cost drivers in business-oriented language


• Problem-solving and innovation mindset to balance performance and cost in AI workloads

Key Responsibilities

• Own and maintain the centralised FinOps framework for tracking cloud, OpenShift, GPU infrastructure and GenAI token consumption across projects, teams and applications


• Drive GenAI token cost optimisation through model selection, routing, caching, prompt and token efficiency, usage controls and cost-per-request analysis


• Optimise OpenShift infrastructure consumption through resource right-sizing, utilisation analysis, workload scheduling, quota management and identification of under-utilised resources


• Drive cloud cost optimisation across Azure services through right-sizing, reservation and commitment planning, workload scheduling, storage optimisation and elimination of idle or unnecessary resources


• Establish and operate chargeback and showback models, tagging, cost allocation and governance standards for accurate project and use-case attribution


• Administer operating and infrastructure budgets, including forecasting, budget tracking, variance analysis and regular management reporting


• Support infrastructure, software and cloud procurement activities, including demand forecasting, quotation evaluation, commercial analysis, contract and commitment planning and purchase requirements


• Build dashboards, alerts and unit-cost metrics to monitor actual and forecast spend, utilisation, token cost and cost efficiency


• Identify budget risks and cost anomalies through ongoing monitoring and analysis


• Partner with platform engineering, ML/LLMOps, IT infrastructure, finance and procurement teams to identify and implement cost optimisation opportunities without compromising platform performance and reliability


• Drive adoption of FinOps practices across the AI platform


• Provide recommendations to management on investment, capacity, sourcing and cost optimisation decisions

Requirements

• Minimum of six years’ experience in cloud infrastructure, platform engineering, FinOps, technology cost management or related operations


• On-site working arrangement

If you have the relevant skills and experience, please apply with an updated CV.