Staff Cloud FinOps Engineer

LE400 Automation Anywhere Software Pvt. Ltd.; India; On-site; INR 2,500,000 - 4,000,000; Full time

Job summary

Automation Anywhere is seeking a highly skilled Multi-Cloud FinOps Staff Engineer to optimize cloud costs across Azure, AWS, and GCP, focusing on AI/GenAI cost governance, Kubernetes workload efficiency, and enterprise chargeback strategies. Location: Bangalore. Reports to: Director Cloud Engineering.

You will lead architectural reviews, implement cost-aware designs, develop Terraform-based governance, and build dashboards for executive visibility and business value realization.

Job description

About Us 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 industrys first Process Reasoning Engine (PRE) and specialized AI agents, combines process discovery, RPA, end-to-end orchestration, document processing, and analyticsall 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 Youll 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 realization
• Preferred knowledge: Azure OpenAI Service, Vector DBs, RAG, GPU optimization, AI inferencing economics
• Tenant-Level Usage Tracking & Chargeback Design tenant-level metering systems for: API usage, AI token consumption, Kubernetes namespaces, GPU consumption, Storage utilization, Data pipeline execution Build: Showback dashboards, Chargeback engines, Department-level cost transparency Ensure accurate tagging, allocation, and reconciliation mechanisms
• Data/Feature Engineering & Pipeline Optimization Architect scalable and cost-optimized data pipelines Optimize: ETL/ELT workloads, Streaming pipelines, Data lake storage tiers, Data retention policies, Query optimization Implement data observability and cost intelligence frameworks prefered knowledge : Databricks, BigQuery

You Will Be a Great Fit If You Have:

• Bachelors or Masters degree in Computer Science, Data Engineering, or related field
• 6+ years of experience in Cloud engineering, FinOps, or DevOps roles
• 3+ years in FinOps or cloud financial governance
• Deep understanding of cloud billing .