Finance Operations Analytics Architect (Agentic AI Expertise) Technology

Citi; Singapore; On-site; SGD 180,000 - 240,000; Full time

Job summary

Citi is seeking a FinOps Analytics leader in Singapore to drive cloud cost optimization and governance. The role covers cost observability platforms, budgeting models, and real‑time cost management for cloud and on‑prem environments.

You will architect autonomous AI workflows, enforce guardrails, and collaborate with finance, engineering and product teams across the Citi tech stack. Responsibilities include optimizing cloud spend, building forecasting dashboards, and ensuring cost transparency

Qualifications

• Exposure to cloud architecture (AWS, Azure, GCP) with hands-on cost optimization experience.
• Strong knowledge of FinOps principles, cost models, 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 provide technical cost advisory across AWS/Azure/GCP.
• Review high-spend services to identify inefficiencies and suggest code and infra changes.
• Promote cost-efficient design, serverless patterns, right-sizing and storage tiering.
• Build cost observability, real-time analytics, forecasting models, dashboards and anomaly detection for cloud budgets.
• Implement tagging standards, cost attribution, chargeback/showback, and governance policies.
• Integrate data from cloud providers, usage logs, telemetry, and AI agent activity streams.
• Oversee Agentic AI workflows and ensure safe, compliant automated optimizations.
• Lead cross-functional collaboration with finance, engineering and product teams on cost goals.

Skills

Cloud cost optimization

FinOps

Cost governance

LLMs & AI

AI architectures

Tools

Azure Copilot Optimization Agent

OpenTelemetry

Job description

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

• 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.”
• 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.
• 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.

Agentic AI Responsibilities

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.
• 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.
• 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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