The FinOps Analytics is a under technical leader responsible for driving cloud cost optimization, building cost‑observability platforms, and enabling proactive cloud financial governance. In addition to core FinOps responsibilities, this role now incorporates Agentic AI architecture, governance, and cost‑control capabilities as organizations shift from traditional dashboards to autonomous optimization systems. Agentic AI introduces autonomous AI agents capable of analyzing data, making decisions, and executing actions at scale—requiring new guardrails, real‑time cost management, and AI-centric FinOps frameworks.
Agentic AI introduces autonomous, reasoning‑capable AI agents that perform tasks, invoke APIs, spin up compute, and make resource decisions independently—requiring a new layer of FinOps oversight.
Don't want to miss the next one?
Subscribe to daily email alerts for roles matching your interests.
Design & Integrate Agentic AI Workflows into FinOps
- Architect and integrate Agentic AI systems that autonomously analyze cloud usage, detect inefficiencies, and propose or execute optimizations.
- Incorporate multi‑agent systems capable of proactive anomaly detection, predictive optimization, and autonomous corrective actions within the cloud and on-prem ecosystem.
Real‑Time AI Agent Cost Visibility & Ownership
- Establish per‑agent cost attribution, including owner tags, budget identifiers, and full traceability of every model invocation or API call.
- Build telemetry pipelines (e.g., OpenTelemetry with cost metadata) capturing cost_per_call, decision logs, and tool usage for all agents.
Budgeting, Guardrails & Autonomous Spending Control
- Design dynamic and iterative budgeting models, replacing static annual budgets with daily/weekly limit enforcement for agentic workflows.
- Implement policy-driven controls (e.g., budget throttles, automated revocation, execution guardrails) to manage microtransaction-level spend driven by autonomous agents.
- Govern agent estates using enterprise-grade tooling (e.g., Microsoft’s Foundry Control Plane) to enforce identity, security, and auditability for AI agent actions.
AI Optimization Agents & Execution Automation
- Leverage or build Citi AI optimization agents (e.g., Azure Copilot Optimization Agent) that automatically analyze performance, compare SKU alternatives, and generate execution-ready automation scripts.
- Oversee the safe implementation of agent-suggested optimizations by validating performance impact and compliance before execution.
FinOps for LLM, Multi-Agent & RAG Architectures
- Manage the cost implications of LLM inference, multi-agent collaboration, and retrieval-augmented generation (RAG) workflows, where token usage and replication can multiply costs significantly.
- Optimize model selection, context length, inference endpoints, and caching strategies to reduce unnecessary LLM consumption.
Full time ------------------------------------------------------
Most Relevant Skills
Please see the requirements listed above. ------------------------------------------------------
Other Relevant Skills
For complementary skills, please see above and/or contact the recruiter. ------------------------------------------------------
Citi is an equal opportunity employer, and qualified candidates will receive consideration without regard to their race, color, religion, sex, sexual orientation, gender identity, national origin, disability, status as a protected veteran, or any other characteristic protected by law.
If you are a person with a disability and need a reasonable accommodation to use our search tools and/or apply for a career opportunity review Accessibility at Citi.
View Citi’s EEO Policy Statement and the Know Your Rights poster.
Similar roles you might like
More openings like this one — take a look before you go.
