{"id":964623,"url":"https://alion.io/job/citi-finops-analytics-assistant-vice-president","title":"Finops Analytics - Assistant Vice President","company":{"id":7841,"name":"Citi","domain":"citi.com","url":"https://alion.io/company/citi","size_band":"5000+","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Workday","truth_index":{"grade":"A","score":100,"open_postings":512,"ghost_share":0.006,"stale_share":0.131,"repost_share":0.072,"time_to_fill_p50_days":9,"computed_at":"2026-11-01T06:10:25Z"}},"role":"Analytics","role_family":"Analytics","seniority":"head","employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Chennai, India","Pune, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":23000,"max_usd":50000,"period":"year","method":"global_role_cell_scaled_by_country","sample_n":404},"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Agentic Workflows","optional":false},{"name":"AI Agents","optional":false},{"name":"AWS","optional":false},{"name":"Azure","optional":false},{"name":"Copilot","optional":false},{"name":"FinOps","optional":false},{"name":"GCP","optional":false},{"name":"LLM","optional":false},{"name":"LLM Guardrails","optional":false},{"name":"Multi-Agent Systems","optional":false},{"name":"OpenTelemetry","optional":false},{"name":"RAG","optional":false}],"status":"live","first_seen_at":"2026-09-16T13:03:36Z","employer_posted_date":"2026-09-16","last_verified_at":"2026-11-01T12:40:39Z","board_verified":true,"closed_at":null,"days_open":45,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":45},"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.\nAgentic 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.\nKey Responsibilities\n1. Cloud Architecture Optimization & Technical Advisory\nConduct reviews of high-spend cloud services to identify inefficiencies.\n\nRecommend code-level and infrastructure changes-including serverless patterns, right-sizing, and storage tiering-to reduce spend.\n\nEnsure engineering teams adopt cost-efficient design standards to prevent cloud and on-prem “tech debt.”\n\n2. FinOps Data, Analytics & Cost Transparency\nBuild cloud cost observability and on-prem analytics frameworks that provide real-time usage and spend insights.\n\nDevelop forecasting models, dashboards, anomaly-detection systems, and financial models to support cloud budgeting.\n\nIntegrate data from cloud providers, usage logs, telemetry, and AI agent activity streams.\n\n3. Governance, Policy Automation & Cloud Financial Controls\nImplement tagging standards, cost attribution, chargeback/showback frameworks, and compliance policies.\n\nManage FinOps governance foundations promoting visibility, accountability, and cross-team alignment.\n\n4. Agentic AI Responsibilities\nAgentic AI introduces autonomous, reasoning-capable AI agents that perform tasks, invoke APIs, spin up compute, and make resource decisions independently-requiringa new layer of FinOps oversight.\nDesign & Integrate Agentic AI Workflows into FinOps\nArchitect and integrate Agentic AI systems that autonomously analyze cloud usage, detect inefficiencies, and propose or execute optimizations.\n\nIncorporate multi-agent systems capable of proactive anomaly detection, predictive optimization, and autonomous corrective actions within the cloud and on-prem ecosystem.\n\nReal-Time AI Agent Cost Visibility & Ownership\nEstablish per-agent cost attribution, including owner tags, budget identifiers, and full traceability of every model invocation or API call.\n\nBuild telemetry pipelines (e.g., OpenTelemetry with cost metadata) capturing cost_per_call, decision logs, and tool usage for all agents.\n\nBudgeting, Guardrails & Autonomous Spending Control\nDesign dynamic and iterative budgeting models, replacing static annual budgets with daily/weekly limit enforcement for agentic workflows.\n\nImplement policy-driven controls (e.g., budget throttles, automated revocation, execution guardrails) to manage microtransaction-levelspend driven by autonomous agents.\n\nGovern agent estates using enterprise-grade tooling (e.g., Microsoft’s Foundry Control Plane) to enforce identity, security, and auditability for AI agent actions.\n\nAI Optimization Agents & Execution Automation\nLeverage 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.\n\nOversee the safe implementation of agent-suggested optimizations by validating performance impact and compliance before execution.\n\nFinOps for LLM, Multi-Agent & RAG Architectures\nManage the cost implications of LLM inference, multi-agent collaboration, and retrieval-augmented generation (RAG) workflows, where token usage and replication can multiply costs significantly.\n\nOptimize model selection, context length, inference endpoints, and caching strategies to reduce unnecessary LLM consumption.\n\n5. Cross-Functional Collaboration & Stakeholder Leadership\nPartner with FinOps Champions, engineering teams, and business stakeholders to translate cloud and AI cost goals into actionable backlogs.\n\nPromote organizational alignment via shared ownership of cloud and and on-prem AI spending across finance, engineering, and operations.\n\nCommunicate complex On-prem, cloud, and AI cost insights clearly to executives and product teams.\n\n6. Continuous Cloud & AI Optimization Strategy\nDrive ongoing cloud and agent-driven optimization initiatives to reduce waste, prevent cost overruns, and maximize ROI.\n\nDevelop long-term cloud, AI, and automation strategy including SKU optimization, licensing, GPU provisioning, and model lifecycle cost management.\n\nRequired Qualifications\nExposure to cloud architecture (AWS, Azure, GCP) with hands-on cost optimization experience.\n\nGood knowledge of FinOps principles, cost models, and cloud financial governance.\n\nUnderstanding of LLMs, multi-agent architectures, RAG workflows, and AI operational cost models.\n\nAbility to design secure, monitored, and budget-controlled environments for autonomous agents.\n\n------------------------------------------------------\nJob Family Group:\nTechnology------------------------------------------------------\nJob Family:\nInfrastructure------------------------------------------------------\nTime Type:\nFull time------------------------------------------------------\nMost Relevant Skills\nPlease see the requirements listed above.------------------------------------------------------\nOther Relevant Skills\nFor complementary skills, please see above and/or contact the recruiter.------------------------------------------------------\nCiti is an equal opportunity employer, and qualified candidates will receive consideration without regard to their race, color, religion, sex, sexual orientation, gender identity, national origin, disability, status as a protected veteran, or any other characteristic protected by law.\nIf you are a person with a disability and need a reasonable accommodation to use our search tools and/or apply for a career opportunity review Accessibility at Citi.\nView Citi’s EEO Policy Statement and the Know Your Rights 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