AI & Cloud FinOps Engineer

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.

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.

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.

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.