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-productive 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.

📌 AI FinOps & Technology Finance Analyst (India)
🏢 Sampoorna Computer People
📍 India