AI & Cloud FinOps Engineer

WTW • Charlotte, NC

3d

About The Position

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.

Requirements

• 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.

Nice To Haves

• 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.

Responsibilities

• 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 supporting the automation of the monthly forecast and variance pack, including AI-drafted commentary, published to stakeholders each month.
• 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.
• Build the alert-to-closure workflow: multi-threshold budget and anomaly alerts routed to a named owner, with response SLAs, escalation, and closure evidence captured automatically.
• Enhance monitoring with automated metrics that lead to meaningful and actionable insights.
• Automate insight-to-action routing so optimization recommendations, anomalies, and trends reach the right owner, are tracked to resolution, and do not quietly expire.
• Automate cost hygiene: detection of orphaned and idle resources triggering lifecycle actions and build the tooling behind our rightsizing practice.
• Help shape the guardrails that stop AI spend running away: real-time visibility, cost controls, step and budget caps on agentic workloads, and a central model-routing layer that enforces model-tier policy, caching and batching without teams having to change their code.
• Build out our FinOps for AI practice: unit economics such as cost per prompt, per outcome, per user and per agent run; model, seat and feature hygiene.
• Apply AI to the practice itself: anomaly explanation, tagging remediation, variance commentary and optimization prioritization.
• Codify and automate best practices, so engineering, product and finance teams can act without queuing behind the central team.

Benefits

• Mental health/emotional wellbeing (including Employee Assistance Program)
• Medical (including prescription drug coverage and fertility benefits)
• Dental
• Vision
• Health Savings Account
• Commuter Accounts
• Health Care and Dependent Care Flexible Spending Accounts
• Company-paid life insurance
• Supplemental life insurance
• AD&D
• Group accident
• Group critical illness
• Group legal
• Identify theft protection
• Wellbeing program
• Adoption assistance
• Surrogacy assistance
• Auto/home insurance
• Pet insurance
• Other work/life resources
• Paid holidays
• Annual paid time off (includes state/local paid leave where required)
• Company-paid disability (short-term and long-term disability)
• Other leaves (e.g., bereavement, FMLA, ADA, jury duty, military leave, and Parental and Adoption Leave)
• Paid Time Off (only included for Washington roles)
• Qualified contributory pension plan (if eligible)
• 401(k) plan with annual nonelective company contribution.
• Non-qualified retirement plans available to senior level colleagues who satisfy the plans’ eligibility requirements.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: No Education Listed