Finance Operations (FinOps) Analytics Architect (with Agentic AI Expertise)

Job Description

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.

Job Responsibility

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.”
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
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
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
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
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
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
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
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
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

Requirements

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

Nice to have

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

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

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