Quick overview
Job description
- Drive Cloud and AI/LLM efficiency
- Own a single, prioritised source of truth for all cost initiatives and drive them to measurable efficiency and margin gains.
- Pressure-test savings claims: verify realised vs. projected savings post-launch, and prevent silent cost regressions from eroding prior wins
- Build cost-attribution discipline: per-service, per-team, per-customer, per-feature tagging and allocation so every dollar has an owner
- Oversee LLM spend across providers: procurement and commitment sizing
- Govern internal AI tooling spend with per-user visibility, quotas, and value assessment
- Build the observability & automation layer
- Treat cost like production telemetry: real-time cost attribution, anomaly detection, alerting, and automated triage
- Automate the toil: cost-triage agents, regression detection, and self-serve attribution so the program scales
- Deep, hands-on understanding of cloud infrastructure and AI/LLM economics
- Proficient in driving efficiency, capacity, or FinOps-adjacent programs
- Eng operations instincts: you think in SLOs, error budgets, telemetry, and postmortems, and you apply that discipline to spend
- Strong SQL and data skills; you can query BigQuery, build a Sigma workbook, and model a scenario
- This role is hybrid (3 days a week in our Bangalore office)
Compensation & Benefits:
Compensation offered will be determined by factors such as location, level, job-related knowledge, skills, and experience. Certain roles may be eligible for variable compensation, equity, and benefits.
We are a diverse bunch of people and we want to continue to attract and retain a diverse range of people into our organization. We're committed to an inclusive and diverse company. We do not discriminate based on gender, ethnicity, sexual orientation, religion, civil or family status, age, disability, or race.
By clicking “Submit Application,” I confirm that I have read the Global Data Privacy Notice and the Applicant Arbitration Agreement, and I agree to the terms.
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