Staff Cloud FinOps Engineer

Automation Anywhere is the leader in Agentic Process Automation (APA), transforming how work gets done with AI-powered automation. Its APA system, built on the industry’s first Process Reasoning Engine (PRE) and specialized AI agents, combines process discovery, RPA, end-to-end orchestration, document processing, and analytics—all delivered with enterprise-grade security and governance. Guided by its vision to fuel the future of work, Automation Anywhere helps organizations worldwide boost productivity, accelerate growth, and unleash human potential. Our Opportunity: We are seeking a highly skilled Multi-Cloud FinOps “Staff Engineer” to contribute to cloud financial optimization, AI/GenAI cost governance, Kubernetes workload efficiency, MLOps optimization, and enterprise chargeback/show back strategy across Azure, AWS, and GCP environments.   This role combines: · Cloud architecture · FinOps governance · AI/ML platform optimization · Data engineering · Financial analytics · Enterprise budgeting & forecasting The ideal candidate will drive cloud profitability, tenant-level usage accountability, and intelligent cost optimization for modern AI-powered platforms. Location: Bangalore Who You’ll Report To: Director – Cloud Engineering You Will Make an Impact By Being Responsible For: Technical Advisory · Conduct deep-dive architectural reviews of high-spend services to identify inefficiencies. · Provide specific code-level and infrastructure recommendations, such as refactoring for serverless, right-sizing containerized environments, and optimizing storage tiering logic. · Advise engineering teams on cost-efficient design patterns during the initial design phase to prevent 'technical debt' in the cloud bill. · Translate high-level savings targets into actionable technical backlogs. · Oversee and develop scripts (e.g., Python, Bash) and Infrastructure as Code (Terraform) for automated governance. · Build and maintain technical 'guardrails' that prevent cost leaks before they occur. · Automate the detection and remediation of orphaned resources, unoptimized snapshots, or inefficient architectural patterns. · Translate complex technical optimization successes into business value metrics for Senior Leadership. · Provide technical feasibility assessments for long-term cloud financial commitments and strategic procurement decisions. Financial Planning, Reporting, Budgeting & Forecasting · Build cloud financial forecasting models · Drive: Budget planning, Forecast variance analysis, Margin optimization, Cost anomaly detection, Capacity planning · Develop KPI frameworks for: Cost per tenant, Cost per transaction, Cost per AI request, Cost per model training run, Gross margin tracking · Partner with Finance and Engineering teams for monthly business reviews · Build executive FinOps dashboards and reporting systems · Present optimization opportunities to leadership · Establish cloud governance standards and compliance controls · Enable data-driven decision making through financial analytics Kubernetes & Container Cost Optimization · Lead Kubernetes FinOps initiatives for enterprise-scale clusters · Optimize: Node utilization, Autoscaling policies, Spot/preemptible workloads, Namespace-level cost visibility, GPU allocation, Multi-tenant clusters · Implement workload rightsizing strategies using: CPU/memory profiling, Idle resource detection, Bin-packing optimization, Scheduling efficiency. · Build tenant-level Kubernetes cost attribution and dashboards Tools Exposure: Kubecost, OpenCost, Prometheus/Grafana, AKS/EKS/GKE, Karpenter, Cluster Autoscaler GenAI & Azure OpenAI Cost Optimization · Optimize: Token consumption, Prompt engineering efficiency, Model selection strategies, Context window utilization, Embedding/vector database cost · Implement: AI governance, AI usage metering, AI quota management, Cost guardrails, Token forecasting models · Analyze AI workload ROI and business value