Live opening · Posted 11 days ago
The key details from the original listing.
Description supplied by the original job listing.
About Deutsche Börse Group:
Headquartered in Frankfurt, Germany, Deutsche Börse Group is a leading international exchange organization and market infrastructure provider. They empower investors, financial institutions, and companies by facilitating access to global capital markets.
Their India centre is located in Hyderabad, serves as a key strategic hub and comprises India’s top-tier tech talent. They focus on crafting advanced IT solutions that elevate market infrastructure and services. Deutsche Börse Group in India is composed of a team of capital market engineers forming the backbone of financial markets worldwide.
FinOps Lead Technical Expert – AI
Role Overview:
This is a senior hybrid role combining deep technical knowledge with financial governance, purpose-built to manage and optimize the growing costs of AI and cloud infrastructure. You will be at the center of technical cost efficiency efforts across 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 cloud utilization, and embed automation into the cloud lifecycle. You’ll collaborate with Business IT teams and central infrastructure teams to steer and implement 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 and optimize costs across the full AI/ML stack — including GPU/TPU compute, model training, inference workloads, and LLM API consumption
Analyze cost and usage data across IaaS, PaaS, and SaaS layers using tools such as Cloud ability, Azure Advisor, and GCP Recommender
Identify and 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
Identify savings opportunities through model optimization techniques such as quantization, distillation, and batching strategies
FinOps Practice Leadership & Governance:
Lead and mature the organization's FinOps culture, governance framework, and operating model across cloud and AI domains
Define and enforce FinOps 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 enable accurate cost 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-source vs. 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 environment (GCP, Azure) — covering infrastructure and AI/ML platform layers
Contribute to the PoC, evaluation, and rollout of optimization platforms such as Turbonomic or equivalent tooling
Integrate cost awareness into CI/CD pipelines to enable cost-conscious engineering at the point of development
Forecasting, Reporting & Accountability:
Build and maintain 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 the financial impact of optimization initiatives, reporting measurable savings against targets
Support FinOps 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 and maintain strong 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, FinOps - DevOps roles.
Strong practical knowledge of AWS, Azure, and/or GCP services.
Hands-on experience with:
Cloud-native optimization tools (Flexera, ServiceNow, Turbonomic etc.)
Automation scripting (Python, Bash, PowerShell)
Solid understanding of cloud billing models and pricing mechanics.
Experience with Cloud ability (Apptio) or equivalent cost management tools.
Work arrangement
No
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