The Fin Ops 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 Fin Ops 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 Fin Ops frameworks.

Key Responsibilities1.

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. Fin Ops 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 Fin Ops 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 Fin Ops oversight.

Design & Integrate Agentic AI Workflows into Fin Ops

• 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., Open Telemetry 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.

Fin Ops 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 Fin Ops 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 Fin Ops 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

• Fin Ops Certified Practitioner / Fin Ops 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.

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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