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AI FinOps Lead

Job in Reigate, Surrey County, RH2, England, UK
Listing for: WeDoTech
Full Time, Part Time position
Listed on 2026-09-30
Job specializations:
  • IT/Tech
    AI Engineer (Applied/Software)
Salary/Wage Range or Industry Benchmark: 80000 - 95000 GBP Yearly GBP 80000.00 95000.00 YEAR
Job Description & How to Apply Below
Job Title:

AI Fin Ops Lead
Salary:
£80,000 – £95,000 + 15% bonus

Location:

Croydon – ideally 2 days per week in the office, with some flexibility for the right candidate
Work Type:
Permanent
Role:
AI investment is growing rapidly, and this is an opportunity to take ownership of how a large, complex organisation manages, measures and maximises the value of that investment.
We’re looking for an AI Economics & Fin Ops Lead to take a highly visible role combining traditional cloud Fin Ops with the emerging world of AI cost management and token economics.
This isn’t a role where you’ll simply produce reports and tell teams what they’ve spent. You’ll work directly with engineering, AI and business stakeholders to understand how AI is being consumed, why costs differ between teams and technology stacks, and whether that investment is delivering genuine business value.
You’ll have the opportunity to help shape how AI economics is managed as adoption accelerates, while continuing to drive optimisation across a significant wider cloud estate.

Responsibilities:

• Take ownership of AI economics and Fin Ops activity across a complex technology environment.

• Analyse AI, token and cloud consumption to understand where money is being spent and the value being generated.

• Work directly with engineering and technology teams to investigate unusual consumption patterns and identify opportunities to improve efficiency.

• Help teams optimise AI usage through areas such as model selection, prompt optimisation, caching, context management and model routing.

• Develop meaningful metrics around AI unit economics, token efficiency and value realisation.

• Support ongoing forecasting and budgeting, identifying potential overspend early and helping teams take action.

• Compare AI models, providers and usage patterns to support better technology and commercial decisions.

• Improve cost allocation, accountability, tagging, showback and chargeback across AI and cloud consumption.

• Continue to drive traditional Fin Ops optimisation, including rightsizing, commitment utilisation, storage, scaling and workload optimisation.

• Build reporting and dashboards that translate complex consumption data into clear insights for technical teams and senior leadership.

• Become a thought leader for AI economics and Fin Ops, helping educate teams and establish best practice as the capability develops.

Required Skills:

• Strong experience within Fin Ops, cloud financial management or a closely related discipline.

• Practical exposure to AI/ML or GenAI cost management and an understanding of token-based consumption.

• Knowledge of how LLM/GenAI pricing and consumption works, including areas such as input/output tokens, caching and different model/provider pricing structures.

• Experience analysing cloud consumption and turning data into tangible optimisation actions and financial outcomes.

• Experience working within a complex cloud environment. Azure is preferred, although experience across other major cloud platforms will be considered.

• Understanding of traditional Fin Ops practices including cost allocation, tagging, forecasting, budgeting and cloud optimisation.

• Strong analytical and reporting capability, with experience using Power BI or similar tooling.

• The confidence to work directly with engineering leaders, technical teams and senior stakeholders, constructively challenging existing approaches where required.

• The ability to take ownership of a developing capability rather than simply operate within an established process.

Why should I apply?
This is an opportunity to get ahead of where Fin Ops is going next.
You’ll combine an established Fin Ops skillset with one of the fastest-developing areas in technology: understanding the economics behind…
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