Key Responsibilities :

1. AI FinOps and Cost Optimization :

- Monitor and analyze AI-related expenditure across cloud AI services, generative AI platforms, large language models (LLMs), APIs, GPU/CPU compute, storage, and model hosting.

- Track costs associated with AI model training, fine-tuning, inference, token consumption, embeddings, vector databases, and AI-powered applications.

- Develop cost allocation and chargeback/showback models for AI and cloud consumption across business units, projects, and product teams.

- Identify opportunities to optimize AI workloads through model selection, prompt optimization, token reduction, caching, batching, and efficient resource utilization.

- Establish unit economics metrics such as cost per inference, cost per 1,000 tokens, cost per AI transaction, and cost per customer or business outcome.

- Partner with technical teams to evaluate cost-performance trade-offs across different AI models, cloud services, and deployment architectures.

2. Cloud and Technology Financial Management :

- Monitor and optimize cloud expenditure across platforms such as AWS, Microsoft Azure, and Google Cloud Platform (GCP).

- Analyze spending on compute, storage, networking, databases, SaaS applications, software licenses, infrastructure, and third-party technology services.

- Track technology consumption against budgets, forecasts, and approved business cases.

- Identify cost-saving opportunities through rightsizing, resource scheduling, reserved capacity, savings plans, license optimization, and elimination of unused resources.

- Support technology contract reviews, vendor spend analysis, renewal planning, and procurement negotiations.

- Evaluate the financial impact of technology architecture changes, cloud migrations, and AI adoption initiatives.

3. Budgeting, Forecasting, and Financial Planning :

- Support annual budgeting, quarterly forecasting, and periodic financial reviews for AI, cloud, and technology portfolios.

- Analyze actual expenditure against budget, identify variances, and provide explanations and corrective recommendations.

- Develop cost models and financial forecasts based on usage trends, business growth, AI adoption, and technology roadmaps.

- Prepare financial business cases for AI initiatives, cloud transformation programs, technology investments, and automation projects.

- Assess ROI, total cost of ownership (TCO), payback periods, and expected financial benefits of AI and technology investments.

- Support financial planning and analysis (FP&A) activities, accruals, purchase order tracking, and technology expense reporting.

4. FinOps Governance and Reporting :

- Develop dashboards and management reports covering AI, cloud, and technology spending, utilization, cost trends, and optimization opportunities.

- Establish cost allocation policies, tagging standards, budgets, alerts, and financial accountability frameworks.

- Track savings initiatives, cost avoidance, realized savings, and optimization benefits.

- Define KPIs and performance metrics for AI and cloud financial management.

- Support FinOps governance reviews and communicate cost insights to finance leaders, engineering teams, and business stakeholders.

- Maintain documentation of cost optimization initiatives, assumptions, forecasts, and financial controls.

5. AI Investment Analysis and Business Value :

- Assess the financial viability of AI use cases, including generative AI, intelligent automation, predictive analytics, and enterprise AI solutions.

- Compare build-versus-buy options and evaluate proprietary models against commercial APIs and open-source alternatives.

- Analyze AI project costs across development, deployment, integration, monitoring, maintenance, and ongoing inference.

- Measure realized business benefits, including productivity improvements, operational savings, revenue impact, and process efficiencies.

- Partner with product and business teams to establish business cases and track benefits realization.

- Support responsible AI investment decisions by incorporating scalability, reliability, security, compliance, and operating costs into financial assessments.

6. Stakeholder and Vendor Management :

- Collaborate with Finance, IT, Engineering, Data Science, AI/ML, Procurement, and business teams to improve cost visibility and spending discipline.

- Work with cloud providers, AI vendors, SaaS providers, and technology partners to review usage, pricing, billing, and contractual commitments.

- Present actionable financial insights and recommendations to senior management.

- Drive awareness of cost-efficient technology usage and promote a culture of financial accountability.

Required Qualifications :

- Bachelor's degree in Finance, Accounting, Economics, Business Administration, Information Technology, or a related field.

- Typically 2 - 5 years of experience in financial analysis, FP&A, cloud cost management, technology finance, FinOps, or a related role.

- Understanding of cloud billing models, technology cost structures, software licensing, and subscription-based services.

- Working knowledge of AI/ML services, generative AI platforms, LLM APIs, and the cost drivers associated with AI workloads.

- Experience with budgeting, forecasting, variance analysis, cost allocation, and financial reporting.

- Strong Excel skills and experience building dashboards or reports using Power BI, Tableau, or similar tools.

- Ability to analyze large datasets, identify spending patterns, and translate findings into actionable business recommendations.

- Strong communication, stakeholder management, problem-solving, and analytical skills.

Preferred Qualifications :

- FinOps Certified Practitioner or equivalent FinOps training.

- Certifications in AWS, Microsoft Azure, Google Cloud, or cloud financial management.

- Experience with cloud cost management tools such as CloudHealth, Apptio Cloudability, Flexera, AWS Cost Explorer, Azure Cost Management, or Google Cloud Billing reports.

- Familiarity with financial planning tools such as Anaplan, SAP, Oracle, Workday Adaptive Planning, or similar platforms.

- Basic SQL and experience with data visualization or analytics tools.

- Understanding of AI cost monitoring, model usage analytics, token pricing, GPU utilization, and LLM observability tools.

- Exposure to technology procurement, vendor negotiations, and cloud commitments.