Fin Ops Lead Technical Expert AI

Role Overview

This is a senior hybrid role combining deep technical knowledge with financial governance, purpose-built to manage andoptimizethe growing costs of AI and cloud infrastructure. Youwill be at the center of technical cost efficiency effortsacross our multi-cloud environment (GCP, Azure) including the full platform stack: cloud infrastructure, AI/ML platforms, and associated API services.

Your mission is to reduce waste, improve cloudutilization, and embed automation into the cloud lifecycle. Youllcollaborate with Business IT teams andcentral infrastructure teamstosteer andimplement recommendations and continuously improve our cloud efficiency posture.

This is a high-impact technical role in a complex environment, where cost optimization needs to be pragmatic, stakeholder-aligned, and automation-driven.

Key Responsibilities

AI Cost Management & Optimization: Monitor andoptimizecosts across the full AI/ML stack including GPU/TPU compute, model training, inference workloads, and LLM API consumption Analyze cost and usage data across Iaa S, Paa S, and Saa S layers using tools such as Cloudability, Azure Advisor, and GCP Recommender Identifyand drive execution of cloud optimization actions, including: Rightsizing compute and storage resources Instance scheduling and automated shutdown policies Tiered storage strategies aligned to data access patterns Commitment-based savings (Savings Plans, Reserved Instances, CUDs) Spot/preemptible instance strategies for AI training workloads Identifysavings opportunities through model optimization techniques such as quantization, distillation, and batching strategies

Fin Ops Practice Leadership & Governance: Lead and mature the organization's Fin Ops culture, governance framework, and operating model across cloud and AI domains Define and enforce Fin Ops policies, standards,



and cost accountability frameworks across all technical teams Drive adoption of cost-conscious practices across Business IT and central infrastructure teams Champion tagging standardization and enforcement to enableaccuratecost attribution, chargeback/show back, and financial insights Contribute to continuous improvement initiatives that embed financial discipline into how cloud and AI services are built and operate

AI Technical Advisory: Serve as a trusted technical advisor on the cost implications of AI architecture decisions, including: Model selection (open-sourcevs. proprietary) Fine-tuning vs. RAG vs. prompt engineering trade-offs Quantization and model compression strategies Batch vs. real-time inference cost profiles Evaluate and advise on AI/ML platforms (Vertex AI, Azure ML, Databricks) from a cost-efficiency and platform value perspective Act as technical counterpart for cloud stakeholders in Business IT reviewing cloud setups, co-designing sustainable architectures, and embedding cost awareness into technical decision-making

Automation & Tooling: Develop and integrate automation scripts and pipelines to support: Anomaly detection and alerting for unexpected cost spikes Scheduled resource optimization (e.g., auto-stop/start policies, Lambda functions) Cost reporting, budget guardrails, and policy enforcement Implement and enforce automated cost governance across the multi-cloud setting (GCP, Azure) covering infrastructure and AI/ML platform layers Contribute to the Po C, evaluation,



and rollout of optimization platforms such as Turbonomicor equivalent tooling Integrate cost awareness into CI/CD pipelines to enable cost-conscious engineering at the point of development

Forecasting, Reporting & Accountability: Build andmaintain AI and cloud cost forecasting models aligned to business growth, usage trends, and model scaling projections Develop executive-level dashboards and reporting that communicate cloud and AI spend clearly and actionably Track and report AI unit economics, including cost per inference, cost per token, and cost per training run Quantify and track thefinancial impactof optimization initiatives, reporting measurable savings against targets Support Fin Ops reporting with technical root cause analysis for cost anomalies and budget variances Provide technical input to improve forecast accuracy and budget alignment across quarterly and annual planning cycles

Stakeholder Management & Collaboration: Translate complex cloud and AI cost data into clear, actionable business insights for senior leadership and non-technical stakeholders Collaborate with Business IT and central infrastructure teams to review cloud usage, co-design cost-aware architectures, and implement optimization recommendations end-to-end Partner with procurement and vendor management on cloud provider negotiations, AI platform licensing, and commitment-based commercial agreements Build andmaintainstrong relationships across engineering, data science, finance, and product teams driving alignment on cost goals and optimization priorities

Key Qualifications & Experience: 3-5 years in Cloud Engineering, Fin Ops -Dev Ops roles. Strong practical knowledge of AWS, Azure, and/or GCP services. Hands-on experience with: Cloud-native optimization tools (Flexera, Service Now, Turbonomicetc.) Automation scripting (Python, Bash, Power Shell) Solid understanding of cloud billing models and pricing mechanics. Experience with Cloudability(Apptio) or equivalent cost management tools.

📌 Senior Associate - Lead Finops Project Specialist - Cloud Cost Management (Hyderabad)
🏢 Deutsche Borse Group
📍 Hyderabad