Senior FinOps Analyst: Cloud & AI Cost Optimizer (Remote)

AgileEngine, LLC.; Tallahassee (FL); Hybrid; USD 140,000 - 190,000; Full time

Benefits offered by this job

Professional growth

Competitive USD-based pay

Exciting projects

Flextime remote/office

Job summary

AgileEngine is seeking a Senior FinOps Analyst to advise engineering teams on cloud costs and optimization, with a schedule that covers APAC business hours and overlaps PST and afternoon CST.

In this role you will triage inquiries via Slack, conduct root-cause analyses, forecast spend with R&D, document findings in Jira, and contribute to showback reporting and cost governance for AI/LLM workloads.

Qualifications

• 4+ years FinOps experience.
• Strong cloud cost optimization.
• Investigate spend anomalies with root-cause analyses.
• Partner-facing with R&D/engineering teams.
• Forecasting with team alignment.
• OpenAI/LLM cost drivers monitoring.
• Cost modeling and tracking investments.
• Documentation and showback reporting.
• APAC hours with PST/CST overlap.
• Upper-intermediate English.

Responsibilities

• Be primary contact for R&D cloud cost guidance via Slack and other channels.
• Engage APAC teams to review monthly cloud spend and surface costs; capture notes.
• Monitor spend for anomalies; conduct root-cause analyses and document in Jira.
• Support monthly forecasting with team cohorts; produce notes.
• Contribute to showback reporting with cost drivers.
• Support Cost Awareness, update wiki.
• Model cost projections and respond to requests via Slack and email.

Skills

FinOps

Cloud cost mgmt

Root-cause analysis

RD/Eng partner

Forecasting

LLM cost drivers

Cost modeling

Jira

Slack

Documentation

Showback reporting

Stakeholder comms

APAC coverage

Tools

Jira

Slack

Job description

AgileEngine is seeking a Senior FinOps Analyst to advise engineering teams on cloud costs and optimization, with a schedule that covers APAC business hours and overlaps PST and afternoon CST.

In this role you will triage inquiries via Slack, conduct root-cause analyses, forecast spend with R&D, document findings in Jira, and contribute to showback reporting and cost governance for AI/LLM workloads.