Associate Director | Hybrid cloud | Bengaluru | Engineering | Hybrid Cloud Engineering

Job description

Job requisition ID :: 107231 Date: Jun 22, 2026 Location: Bengaluru Designation: Associate Director Entity: Deloitte Touche Tohmatsu India LLP **Associate Director | Hybrid cloud | Bengaluru | Engineering | Hybrid Cloud Engineering** - **Job requisition ID** : 107231 - **Location**: Bengaluru - **Entity**: Deloitte Touche Tohmatsu India LLP **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**