Finance Operations Analytics Architect (Agentic AI Expertise) Technology

Citi; New York (NY); Hybrid; USD 117,000 - 164,000; Full time

Job summary

Citi is seeking a Finance Operations Analytics Architect (Agentic AI Expertise) within Technology to lead cloud cost optimization, build cost observability platforms, and govern AI-enabled FinOps.

The role combines cloud architecture with Agentic AI governance, real-time cost visibility, budgeting controls, and cross-functional collaboration to drive efficiency across cloud and on‑prem environments.

Qualifications

• Experience in cloud architecture with cost optimization and governance.
• Strong knowledge of FinOps principles and cloud financial governance.
• Understanding of LLMs, multi-agent architectures, RAG workflows, and AI cost models.
• Ability to design secure, budget-controlled environments for autonomous agents.

Responsibilities

• Optimize cloud architecture and advise engineering for cost efficiency.
• Build cloud cost observability, forecasting models, dashboards, and anomaly detection.
• Implement tagging standards, cost attribution, chargeback/showback, and governance policies.
• Design and integrate Agentic AI workflows with FinOps oversight and guardrails.
• Ensure real-time cost visibility with per-agent cost attribution and telemetry pipelines.
• Drive budgeting, dynamic controls, and cross‑functional collaboration on cloud and AI spending.

Skills

Cloud architecture

FinOps

LLMs & multi-agent systems

Autonomous agent governance

Job description

Finance Operations Analytics Architect (Agentic AI Expertise) Technology

Job Req Id:

26983560

Location(s):

Singapore, Singapore, Singapore

Job Type:

Hybrid

Posted:

Aug. 17, 2026

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Job Overview

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.

Key Responsibilities

1. Cloud Architecture Optimization & Technical Advisory

• Conduct 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

• 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 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

• Exposure to cloud architecture (AWS, Azure, GCP) with hands‑on cost optimization experience.

• Good knowledge of FinOps principles, cost models, and cloud financial governance.

• 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.

Job Family Group:

Technology

Job Family:

Infrastructure

Time Type:

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.

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