Cloud & Secure Agentic AI Architect
Most enterprise AI never clears the gap between a convincing demo and a passing audit. For twenty years I have architected the systems that do — zero-trust, governed, multi-agent platforms carrying real workloads for Fortune 500 operators across automotive, healthcare, retail, manufacturing and financial services.
Executive matrix
Every figure below comes from delivered work — revenue closed, cost removed, workloads moved, systems shipped.
Azure consumption won through executive architecture sessions, deep-dive workshops and competitive proof-of-concept engagements.
Recurring spend removed through migration, licence elimination, intelligent automation and right-sizing.
Fortune 500 and upper mid-market operators in automotive, healthcare, retail, legal and financial services.
500+ virtual machines and 200+ applications migrated at 99.9% uptime, with zero data loss across eight tested DR events.
Built on Azure AI Foundry, Azure OpenAI and Semantic Kernel. Production systems, not proofs of concept.
Across contested enterprise engagements, backed by 100+ technical workshops and architecture deep dives.
Capability
Six disciplines that decide whether enterprise AI becomes an operating asset or an expensive experiment.
01
Agent fleets designed the way privileged systems are designed: identity-scoped permissions, tool-access governance, human-in-the-loop checkpoints and deterministic rollback. Built on Azure AI Foundry, Azure OpenAI and Semantic Kernel.
15+ production systems · 50% less manual effort
02
Least privilege as the default across VNet, NSG, ExpressRoute, Azure Firewall, Key Vault and Entra ID — engineered against SOC 2, HIPAA and PCI DSS control requirements rather than retrofitted to them.
85% fewer security incidents · 70% fewer vulnerabilities
03
Moving models from pilot to P&L: grounding and retrieval design, evaluation pipelines, drift and hallucination telemetry, and the governance evidence an audit committee will actually accept.
45% lift in customer engagement · full Responsible AI compliance
04
Mainframe and monolith to AKS, Azure Functions and Container Apps — sequenced so the business keeps running while the platform changes underneath it. Including grid-scale and disaster-response systems under adversarial conditions.
700+ workloads migrated · 40% infrastructure cost reduction · 3× scale
05
Real-time governance of the unit economics of inference, so AI spend stays legible as agent volume grows. Published as a peer-reviewed IEEE framework with an open-source reference implementation.
35% average reduction in monthly cloud spend
06
Platform engineering that compounds: infrastructure-as-code, CI/CD that turns release windows into release moments, and AI-assisted development adopted across whole engineering organisations.
Deployments 3 weeks → 2 hours · 50+ teams enabled
Experience
From writing the code, to designing the system, to advising the executives who fund it.
I'm Sandip Patel. I have spent twenty years designing secure, enterprise-grade platforms — the last five at Microsoft, architecting governed agentic AI for organisations under real regulatory scrutiny. I started as an engineer in Mumbai in 2006, and I still read the diagrams before I read the deck.
Microsoft · Frisco, Texas · IEEE Senior Member More about me
Enterprise AI and cloud architecture
September 2023 – Present · Dallas–Fort Worth, Texas
Architect and ship enterprise AI on Azure AI Foundry, Azure OpenAI and Semantic Kernel, with security, governance and regulatory posture designed in from the first diagram. Named owner on engagements where the technical outcome decided the customer's platform.
April 2021 – September 2023 · Plano, Texas
Ran migration and secure-landing-zone programmes for 30+ organisations, and led the technical motion — workshops, proofs of concept, competitive architecture — that converted them.
Technology Architect · Plano, Texas
Architected Toyota North America's cloud supply-chain platform, stood up the DevOps and infrastructure-as-code practice from nothing, and owned disaster recovery that was tested rather than documented. Led a team of 12 engineers and 3 QA specialists.
Cybage · 3i Infotech · Intelegain · Soigné Technologies
Enterprise application design in ASP.NET, C# and SQL Server; financial-services systems including ICICI Bank's logical access management gateway; e-commerce and content platforms serving 50,000+ daily users. The ground truth that later made the architecture credible.
Applied research
Frameworks an enterprise can pick up and apply — peer-reviewed, indexed by Crossref, one of them already cited independently by researchers with no connection to me or my employer.
Ten runtime controls, 232 coded source assignments and four maturity levels, drawn from an 80-record evidence base and a six-family standards crosswalk.
A reference architecture for AI in grid environments operating under adversarial telemetry, degraded connectivity and regulatory scrutiny.
An autonomous framework for real-time cost governance in AI-infused cloud architectures, with a working open-source reference implementation.
Market voice
Seven features across six outlets in 2026 — every one earned editorial, none paid or employer-placed. Journalists come for the part most vendors will not say out loud: what enterprise AI actually costs, where it actually breaks, and what has to be true before it ships.
Ecosystem
Where new architecture gets decided, someone has to separate the rigorous from the merely confident. Increasingly, institutions ask me to be that person.
Invited to evaluate submissions for IEEE Transactions on Cloud Computing and five international conferences, with programme-committee seats at three of them.
Selected to adjudicate the NASA International Space Apps Challenge in Los Angeles, Technovation Girls 2026 Gold, and HackAI at UT Dallas — assessing architecture, originality and feasibility.
The highest grade an IEEE member may apply for directly — requiring a decade of significant performance and endorsement by a panel of Senior and Fellow members.
Executive advisory
A small number of engagements each year, chosen for the difficulty of the problem rather than the size of the logo. If any of these describe where you are, the conversation is worth having.
01
A structured assessment of your agentic AI or cloud estate against zero-trust, governance and unit-economics criteria — with the findings written so a board can act on them.
02
A candid session for leadership on where enterprise AI is genuinely heading, what it will cost, and what it means for your operating model over the next four quarters.
03
Conference and industry-event speaking on secure agentic AI, zero-trust architecture, AI governance and the economics of inference at scale.
04
Analyst briefings and journalist enquiries on enterprise AI adoption, cloud economics and critical-infrastructure security.
Tell me what you are trying to build and what is currently standing in the way. I reply to everything that is specific.
Views expressed here are my own and not those of my employer.