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Willis Towers Watson is evolving its FinOps practice to automate cloud and AI cost controls. The role focuses on planning, monitoring, optimizing, and evolving FinOps for Azure and AI at scale.
You will automate cost estimates, data ingestion, attribution, and alerting, while enabling governance and cost hygiene across a global tech estate. The ideal candidate has 5+ years in cloud cost management, strong Azure tooling experience, and a track record of automation that replaces recurring manual
WTW is evolving the FinOps practice that keeps our AI and cloud spend visible, accountable and efficient. This role will help drive the evolution from traditional cloud FinOps to Tokenomics, and the automation of that practice.
You will build the controls, reporting, and automation that let our FinOps practice run without manual effort across Azure and AI, and you will help stand up our FinOps for AI capability from a standing start. Small core FinOps team, global estate, and work that is visible to our most senior technology leaders.
The Role
We focus on 4 core areas: how we plan spend, how we monitor it, how we optimize it, and how the practice itself evolves.
Plan: design cost in before it is spent
Automate design-time cost estimates, so the cost of a workload or an AI feature is understood before it is built.
Support continuous forecasting by support the automation of the monthly forecast and variance pack, including AI-drafted commentary, published to stakeholders each month.
Monitor: make cost visible without manual effort
Automate the ingestion, normalization and allocation of cloud and AI cost data, aligned to FOCUS, so that reporting maintains itself instead of being rebuilt each month.
Instrument AI cost attribution at source; Team, product, feature and environment on every call so that token and model consumption can be attributed, shown back and charged back.
Optimize: turn insights into action
Evolve: grow the practice, especially for AI
The Requirements
Essential
Typically 5+ years in cloud cost management, cloud engineering, platform engineering or a closely related discipline.
Strong Microsoft Azure experience, including the cost management tooling. Awareness of AI services such as Azure Foundry.
Automation engineering capability: for example Python together with Azure Logic Apps and Functions, or close equivalents, and a track record of replacing recurring manual processes with reliable, monitored automation.
Strong data and query skills with experience gathering and maintaining cost data, ideally against FOCUS-aligned datasets.
Understanding of AI cost drivers across different vendors (e.g. GitHub, Anthropic, OpenAI): token consumption, model selection, inference versus training, provisioned throughput, and usage-based pricing models.
Working knowledge of the FinOps Framework in an enterprise setting, including how cost allocation, showback and chargeback work in practice.
Ability to translate cost data into something that lands with both engineers and finance, including reporting in Power BI or similar.
The communication skills to bring engineering, product and finance teams along on a FinOps cultural transformation.
A track record of identifying and delivering cost optimization.
Desirable
FinOps Certified Practitioner, or working towards it.
Experience with a commercial multicloud FinOps platform (e.g. Cloudability or CloudHealth).
Exposure to AI guardrails and governance — rate limiting, budget controls, policy-as-code.
Integration experience with alerting and workflow tooling such as Microsoft Teams and ServiceNow.
Experience beyond Azure (AWS, OCI or GCP) and with SaaS cost management.
What success looks like
In your first 12-18 months, we would expect to see:
Tagging compliance held above 95%, sustained by automation rather than manual clean-up.
Optimization recommendations actioned and measured, with nothing expiring unseen.
Budget and anomaly alerts closed through the automated workflow, each with a complete audit trail.
A material reduction in the manual effort behind monthly reporting and forecasting.
A working AI unit-economics view that product teams actually use to make build decisions.
Note: Employment-based non-immigrant visa sponsorship and/or assistance is not offered for this specific job opportunity.
Compensation and Benefits
Base salary range and benefits information for this position are being included in accordance with requirements of various state/local pay transparency legislation. Please note that salaries may vary for different individuals in the same role based on several factors, including but not limited to location of the role, individual competencies, education/professional certifications, qualifications/experience, performance in the role and potential for revenue generation (Producer roles only).
Compensation
The base salary compensation range being offered for this role is $130,000.00-$140,000.00 USD annually. This role is also eligible for an annual short-term incentive bonus.
Company Benefits
WTW provides a competitive benefit package which includes the following (eligibility requirements apply):
Pursuant to the San Francisco Fair Chance Ordinance and Los Angeles County Fair Chance Ordinance for Employers, we will consider for employment qualified applicants with arrest and conviction records.
This position will remain posted for a minimum of three business days from the date posted or until sufficient/appropriate candidate slate has been identified.