Job Title:  Associate Director | Hybrid cloud | Bengaluru | Engineering | Hybrid Cloud Engineering

Job Title: Associate Director – FinOps & Tokenomics SME (AI Infrastructure)

Role Overview

We are seeking an experienced Associate Director-level FinOps and Tokenomics Subject Matter Expert (SME) to lead cost optimization, financial governance, and economic modeling for AI/ML and GenAI infrastructure platforms.

This role will bridge cloud FinOps, AI workload economics, GPU/accelerator cost optimization, and token-based pricing models, enabling efficient, scalable, and sustainable AI adoption across enterprise environments.

Key Responsibilities

1. AI Infrastructure FinOps Leadership

• Drive end-to-end FinOps strategy for AI platforms across hyperscalers (Azure, AWS, GCP) and hybrid environments
• Define and operationalize cost governance frameworks for:
• GPU / TPU workloads
• LLM inference and training pipelines
• Data pipelines, vector DBs, and orchestration layers
• Implement unit economics models (cost per inference, cost per token, cost per training run)
• Lead budgeting, forecasting, and variance analysis for AI spend

2. Tokenomics & AI Pricing Strategy

• Design and implement token-based pricing models for:
• Generative AI APIs (LLMs, embeddings, fine-tuning)
• Multi-tenant AI platforms and internal chargeback models
• Analyze and optimize:
• Token consumption patterns
• Prompt efficiency and cost-to-value ratios
• Cost of orchestration (RAG, agents, pipelines)
• Develop economic frameworks for AI consumption:
• Token vs compute vs latency trade-offs
• ROI models for GenAI deployments
• Support product teams in defining commercial pricing strategies for AI offerings

3. Cost Optimization & Engineering Collaboration

• Partner with architecture and engineering teams to:
• Optimize model selection (open vs closed, fine-tuned vs base)
• Improve prompt engineering for cost efficiency
• Implement caching, batching, and routing strategies
• Lead initiatives on:
• GPU utilization optimization
• Spot/reserved/committed usage strategies
• Model distillation and quantization for cost reduction
• Drive FinOps maturity across AI lifecycle (build → deploy → scale)

4. Governance, Observability & Tooling

• Establish AI cost observability frameworks:
• Token usage telemetry
• Cost per workload / use case dashboards
• Define and implement:
• Chargeback / showback models
• Cost allocation across BUs, products, or tenants
• Leverage tools such as:
• Azure Cost Management, AWS Cost Explorer
• FinOps platforms (Apptio, CloudHealth, CloudZero)
• AI cost tracking tools (e.g., LangChain observability, custom telemetry)
• Define policies, guardrails, and KPIs for responsible AI spend

5. Strategic Advisory & Stakeholder Engagement

• Act as a trusted advisor to CxOs, product leaders, and platform teams
• Translate technical AI cost drivers into business impact and financial insights
• Lead AI value realization discussions (ROI, TCO, business case development)
• Build enterprise GTM narratives around:
• Sustainable AI adoption
• FinOps for GenAI
• Tokenomics-driven cost strategies

6. Thought Leadership

• Develop frameworks, whitepapers, and POVs on:
• AI FinOps maturity models
• Tokenomics benchmarks and best practices
• AI cost optimization patterns
• Contribute to industry forums, client workshops, and internal capability building

Required Qualifications

Experience

• 12–15+ years of experience across:
• Cloud FinOps / Cloud Economics
• AI/ML platforms or data engineering
• Technology consulting or enterprise architecture
• Strong experience with hyperscaler cloud pricing models and cost optimization
• Proven exposure to Generative AI / LLM ecosystems and cost drivers

Core Skills

• Deep understanding of:
• FinOps principles (allocation, optimization, governance)
• AI infrastructure (GPUs, training/inference pipelines, vector DBs)
• Token-based pricing models (OpenAI, Azure OpenAI, Anthropic, etc.)
• Ability to build:
• Cost models (TCO, ROI, unit economics)
• Forecasting and simulation models for AI workloads
• Strong analytical and stakeholder communication skills

Technical Skills

• Cloud Platforms: Azure, AWS, GCP
• On premises Data Centres
• AI/ML Stack:
• LLM APIs, embeddings, fine-tuning
• Frameworks like LangChain, Semantic Kernel (preferred)
• Data & Analytics:
• SQL, Python (for cost modeling and analysis)
• Visualization tools (Power BI, Tableau)

Leadership & Consulting Skills

• Executive presence and storytelling
• Ability to lead cross-functional teams
• Strong program management and delivery leadership
• Experience in client-facing advisory roles is highly preferred

Preferred Qualifications

• Certifications:
• FinOps Certified Practitioner / Professional
• Azure / AWS Architect certifications
• Experience defining AI platform monetization strategies
• Exposure to multi-cloud + hybrid AI deployments