Staff Engineer, Cloud Engineering

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 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 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:

·Bachelor’s or Master’s 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 models, pricing structures, and cost optimization strategies

·Hands-on experience with AWS Cost Explorer, Azure Cost Management, GCP Billing, and FinOps tools like CloudHealth, etc.

·Strong analytical skills with proficiency in Data & Visualization: SQL, Python, Power BI / Tableau / Grafana, Cost analytics dashboards (cloudhealth, Aptio, cloudzero)

·FinOps Certified Practitioner or similar certification is a plus

·End-to-end understanding of how cloud-based web applications work and their architecture

·Experience with Docker and Kubernetes in production

·Experience with automation tools like Terraform or Ansible

·Exposure to AI platform economics

Ready to Revolutionize Work?

This is an opportunity to work with a global, passionate team pioneering technology that’s redefining the way people work, everywhere.  Join us and discover the many ways that you can have an impact, achieve your potential, and go be great.