Finance Operations (FinOps) Analytics Architect (Agentic AI Expertise) - Vice President

The FinOps Analytics Architect is a senior 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.

Key Responsibilities

1. Cloud Architecture Optimization & Technical Advisory

• Conduct deep architectural reviews of high-spend cloud services to identify inefficiencies.
• Recommend code-level and infrastructure changes-including serverless patterns, right-sizing, and storage tiering-to reduce spend.
• Ensure engineering teams adopt cost-efficient design standards to prevent cloud and on-prem tech debt.

2. FinOps Data, Analytics & Cost Transparency

• Build cloud cost observability and on-prem analytics frameworks that provide real-time usage and spend insights.
• Develop forecasting models, dashboards, anomaly-detection systems, and financial models to support cloud budgeting.
• Integrate data from cloud providers, usage logs, telemetry, and AI agent activity streams.

3. Governance, Policy Automation & Cloud Financial Controls

• Develop automated governance scripts and IaC controls (Python, Bash, elasticsearch, etc) for proactive enforcement.
• Implement tagging standards, cost attribution, chargeback/showback frameworks, and compliance policies.
• Manage FinOps governance foundations promoting visibility, accountability, and cross-team alignment.

4. Agentic AI Responsibilities

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.

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.

5. Cross-Functional Collaboration & Stakeholder Leadership

• Partner with FinOps Champions, engineering teams, and business stakeholders to translate cloud and AI cost goals into actionable backlogs.
• Promote organizational alignment via shared ownership of cloud and and on-prem AI spending across finance, engineering, and operations.
• Communicate complex On-prem, cloud, and AI cost insights clearly to executives and product teams.

6. Continuous Cloud & AI Optimization Strategy

• Drive ongoing cloud and agent-driven optimization initiatives to reduce waste, prevent cost overruns, and maximize ROI.
• Develop long-term cloud, AI, and automation strategy including SKU optimization, licensing, GPU provisioning, and model lifecycle cost management.

Required Qualifications

• Expertise in cloud architecture (AWS, Azure, GCP) with hands-on cost optimization experience.
• Strong mastery of FinOps principles, cost models, and cloud financial governance.
• Experience with Python, SQL, Terraform/IaC, cloud billing datasets, and telemetry instrumentation.
• Understanding of LLMs, multi-agent architectures, RAG workflows, and AI operational cost models.
• Ability to design secure, monitored, and budget-controlled environments for autonomous agents.

Preferred Qualifications

• FinOps Certified Practitioner / FinOps Certified Professional.
• Experience with AI agent platforms such as Azure Copilot Optimization Agent or enterprise agent governance systems.
• Background in MLOps, AI Systems Architecture, or autonomous AI engineering.

#LI-Hybrid
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Job Family Group:
Technology
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Job Family:
Infrastructure
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Time Type:
Full time
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Most Relevant Skills
Please see the requirements listed above.
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Other Relevant Skills
For complementary skills, please see above and/or contact the recruiter.
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