Principal FinOps & Cloud Architect — 15 years across Azure, AWS & GCP, now building the bridge between cloud cost accountability and AI platform governance.
I design cost-aware, secure, governance-ready cloud and AI platforms — turning multi-cloud spend, token economics, and policy frameworks into decisions leadership can trust. Based in Bengaluru, working with global teams across India, Europe and the US.
"Governance should be a platform accelerant, not a control function — that's the principle behind every framework I build."
— On governance-as-enablement, the philosophy behind ContextOpsA rare combination: deep multi-cloud architecture and FinOps execution, paired with an early, deliberate move into AI governance — and the enablement mindset to lead through it.
Delivered up to ~30% multi-cloud cost savings through tagging, chargeback, commitment strategy and lifecycle automation — repeatable, audited results across AWS, Azure and GCP.
Already optimising token-based AI/GenAI workloads (Azure OpenAI, AI Search) and pursuing AIGP + FinOps for AI certifications — positioned ahead of the curve as AI governance becomes board-level.
Not a policy-only voice — a hands-on architect who embeds governance, tagging and cost controls directly into Terraform, CI/CD and platform design, then reports it in language finance and engineering both trust.
Frameworks — including my own ContextOps approach — designed to make governance a platform accelerant for engineering teams, not a control checkpoint.
Power BI / Excel dashboards that translate cloud and AI spend into decisions finance, engineering and leadership can act on together.
FinOps Foundation contributor, "Ask Anything about Cloud" sessions, and ongoing mentoring of architects on cost-aware design.
Four areas of focus — each backed by delivery, not self-assessment. The supporting evidence lives in Experience and Projects below.
Multi-cloud cost governance, tagging, chargeback/showback and commitment planning — the core discipline behind 15 years of delivery and ~30% average savings.
Secure, scalable IaaS/PaaS design with cost and compliance embedded into the platform from day one — not bolted on afterward.
Token-based cost visibility, model-routing economics and cost-performance tiering — applied to live Azure OpenAI and AI Search workloads, and extending into AIGP and FinOps for AI.
Translating cloud and AI spend into decisions leadership can act on — and building the community and mentoring practices that make governance stick.
15 years, one continuous thread: making cloud platforms scalable, secure and financially accountable — now extending into AI.
Led multi-cloud FinOps across AWS, Azure & GCP — establishing visibility, cross-billing and commitment planning. Automated idle-resource detection and lifecycle scheduling, enforced tagging compliance and policy-as-code for accurate showback/chargeback, and optimised AI workload costs through model selection and tier rightsizing.
This engagement is the working proof point behind the FinOps × AI Governance approach — extending directly into AI & GenAI Workload Economics and the governance credentials in Certifications.
A foundation across FinOps, multi-cloud architecture, automation and governance — built over 15 years.
FinOps Foundation
FinOps Foundation
FinOps Foundation
Data Protection & Privacy
Securiti
Amazon Web Services
Google Cloud
HashiCorp
CloudBees
Oracle
Red Hat
Foundation in systems, databases and software engineering — the technical base for a 15-year career spanning DBA, DevOps, Cloud Architecture, and FinOps.
Over the next two years, I'm positioning at the intersection of Cloud FinOps, AI infrastructure economics, and AI governance — helping organisations design and operate AI and cloud platforms with strong financial accountability, cost-aware architecture, and responsible governance. That includes the financial and governance implications of GPUs, tokens, model routing and retrieval pipelines on cost, risk and operational sustainability.