AI systems are easy to ship. Secure Production-grade AI is not.
20+ years Building production-grade AI systems & applications with experience at Uber · Delivery Hero · foodpanda · SWVL · Alibaba.
Most AI works in a demo. Very few systems are secure, governed, observable and reliable enough to run a business on. I'm a Principal AI & Enterprise Architect and fractional CTO — I close that gap, architecting production-grade AI across security, scalability, governance, observability, performance and cost, red team and blue team, offensive and defensive.
Production-Grade AI: Secure · Governed · Observable · Scalable · Reliable · Fast · Cost-Efficient
# current focus role = "Principal AI & Enterprise Architect" also = "AI Security · Production-Grade AI · Fractional CTO" teams = ["red team", "blue team", "offensive", "defensive"] guardrails = ["NeMo Guardrails", "OPA", "prompt-injection / jailbreak filters"] pillars = ["scalability", "security", "reliability", "observability", "governance", "cost", "data / RAG security", "performance", "DevSecOps"] governed_to = ["EU AI Act", "ISO/IEC 42001", "NIST AI RMF"] availability = "open — remote worldwide"
Build faster. Don't build your next vulnerability.
AI can turn an idea into working software in hours. But working doesn't mean secure, resilient or production-ready. Vibe coding and AI coding assistants are accelerating software delivery — and changing the security equation. AI-generated code, APIs, agents, permissions, data flows and infrastructure still need engineering judgment, security validation and production controls before they're trusted with real users and real data.
Speed is no longer the differentiator. Turning what AI builds into a trusted production system is. I bridge AI engineering, enterprise architecture, cybersecurity and production engineering — assessing, attacking, hardening and productionizing what AI, or your own team, builds.
The gap between an AI demo and an AI system you can run a business on.
Anyone can build an AI agent. The difficult part is making it secure, governed, scalable, observable, reliable and cost-efficient enough to run in production. Same system, different engineering — this is the work that happens in between.
Every one of these is a conversation I have often
Most of the risk in enterprise AI isn’t the model — it’s everything around it. Here’s where I come in.
Your agent can access your systems. Who controls what it's allowed to do?
Least-privilege tool authorization, default-deny permissions and human-approval gates for high-impact actions — so an agent's blast radius is bounded by design, not by luck.
Your agents can be manipulated, abused or leak sensitive information
Authorized red-team assessments mapped to the OWASP Top 10 for LLM & Agentic Applications and MITRE ATLAS — evidence-backed findings, not a checklist.
Your AI works in a demo — but not under production load
Production-grade orchestration — reliability, retries, graceful degradation and cost controls built in from day one, not bolted on after the first incident.
Nobody knows why your AI system behaved the way it did
End-to-end tracing, evaluation and audit trails — so every decision an agent makes is explainable after the fact, not a mystery.
Your RAG system answers questions. Can it expose documents a user isn't authorized to see?
Permission-aware retrieval, poisoning detection and citation verification — so answers respect the same access controls as the underlying documents.
Your AI infrastructure cost is growing faster than your business
Model routing, semantic caching and token optimization — so cost per task is known and controlled, not discovered on next month's invoice.
Six outcomes, not thirty deliverables
Every engagement maps to one of these six outcomes — the result you need, not a menu of unrelated services.
Production-Grade AI Architecture
Turn a working prototype into an architecture that survives real production load, failure and change.
- Scalable, Stateless Architecture
- Reliability & Resilience
- Cloud (AWS/GCP/Azure) & Kubernetes
- Latency & Performance Engineering
- CI/CD & DevSecOps
AI Security & Agentic Security
Threat-model and harden your LLMs, RAG, agents and MCP tools before an attacker — or your own agent — finds the gap.
- Prompt-Injection & Jailbreak Defense
- Agent & Tool Permissioning
- MCP / A2A Security
- Red Team & Blue Team
- OWASP LLM & Agentic Top 10
- MITRE ATLAS
AI Governance & Compliance
Build the audit trail and control set that lets you say yes to AI without betting the company on it.
- EU AI Act Readiness
- NIST AI RMF
- ISO/IEC 42001
- Model & Data Lineage
- Policy Engines (OPA / Rego)
- Auditability
AI Observability & Evaluation
See what your agents actually do — before your customers or your board find out the hard way.
- OpenTelemetry Tracing
- Hallucination & Quality Evals
- Token & Cost Visibility
- Agent Behavior Monitoring
- Incident Analysis
AI Cost & Scale Engineering
Know the cost per task, per user, per workflow — and route, cache and right-size until it stays that way.
- Model Routing (vLLM / Triton)
- Semantic Caching
- Token Optimization
- Autoscaling
- FinOps for AI
Fractional CTO / AI Architecture Advisory
Senior technical leadership for AI strategy and architecture — without a full-time executive hire.
- Fractional / Interim CTO
- Principal Architect Embed
- Architecture Review
- Build-vs-Buy & Vendor Strategy
- Team Mentorship
Before attackers test your AI, I do.
Authorized AI red-teaming and runtime defense — tested the same way an adversary would, so the first real attack isn't the first real test. AI security is not a bullet point here; it's the lens every architecture is designed through.
Authorized engagements only. No claim of absolute security — the goal is measurably reducing risk, evidenced by findings and retests, not guarantees.
Read the full breakdown: AI Security · Agentic AI Security.
Ten pillars, on every system I design or review
My own standard for answering one question honestly: is this AI system actually ready for production? Every AI system I design or review is evaluated across the same ten dimensions, from the first diagram to production sign-off. They run through every case study below.
Scalability
Horizontal scale, stateless services, sharding, load-aware autoscaling.
Security
Zero-trust, RBAC/ABAC, secrets management, least privilege, defense in depth.
AI Security / Guardrails
NeMo Guardrails, prompt-injection & jailbreak defense, tool sandboxing, HITL.
Reliability & Resilience
Fallbacks, retries, circuit breakers, graceful degradation, DR.
Observability
OpenTelemetry traces, metrics, logs, evals and cost in one pane.
Governance
AI Act / ISO 42001 / NIST AI RMF, audit trails, model & data lineage.
Cost Optimization
Routing by cost, caching, right-sized models, budget guardrails.
Data & RAG Security
Permission-aware retrieval, poisoning detection, citation verification, isolation.
Performance
Latency budgets, streaming, batching, semantic caching, profiling.
Maintainability / DevSecOps
IaC, CI/CD, SBOM & supply-chain scanning, typed contracts, tests.
More on how this applies end to end: Production AI.
If any of this sounds familiar, we should talk
Prioritized for teams in the GCC, Europe, Australia and the US — remote engagements worldwide.
Founders & CTOs shipping an AI product
You've shipped the first version. Now investors, customers or auditors are asking whether it's actually production-ready.
Enterprises adopting agentic AI
You're moving from single LLM calls to autonomous, tool-using agents — and the blast radius just got a lot bigger.
Companies needing AI security & red-teaming
Your AI is live, and nobody outside your own team has ever tried to break it.
Companies preparing for AI governance / EU AI Act
Legal and compliance are asking questions about your AI systems that engineering can't yet answer.
Companies with a growing AI bill
Your AI spend is climbing faster than usage, and nobody can tell you why.
Companies needing senior leadership, not a full-time hire
You need principal-level AI and architecture judgment — without a full-time executive headcount.
Architecture & product design, not slideware
Independent architecture and product work delivered through Spire Digi Solution (RetailGPT with Datacue — Sanabil Venture) — implementation-ready blueprints and shipped systems.
AI Agent Security & Runtime Defense Platform
A controlled environment for finding out how your agents, RAG and MCP tools actually fail — before an attacker does.
SENTINEL AI — Multi-Agent Security Assessment Platform
500 structured security scenarios across 20 AI-specific domains, run by a fleet of specialized security agents.
AI + Cyber Red Team Command Center
One command center that correlates AI-native attack paths with conventional cyber findings — not two disconnected tools.
Enterprise Agentic AI Operating Platform
The production backbone for building, orchestrating and operating enterprise AI agents — not another agent demo.
Enterprise AI Gateway & Model Router
One control plane for every LLM call in the org — routing, cost, reliability and security in one place.
Secure Enterprise RAG / Knowledge Intelligence Platform
RAG that respects permissions, cites its sources, and doesn’t get poisoned by a bad document.
AI Evaluation & Observability Platform
Because “it felt fine in testing” isn’t a release process for a probabilistic system.
RetailGPT — Agentic Retail Intelligence Platform
Ask retail data a question in plain English and get a governed, cited, multi-format answer back — safely.
20+ years, hands-on, from startups to global platforms
Principal architect, CTO and forward-deployed engineer — owning solution architecture, AI / security strategy and end-to-end delivery.
Principal — AI & Security Architect
Lead architecture and delivery of agentic AI platforms, AI security gateways and cloud-native solutions for UAE / GCC enterprise clients; cross-functional teams of 10+.
Technology Leadership & Solution Architect (AI & Security)
Architected RetailGPT — an agentic AI retail-analytics platform on GCP (Gemini, GPT-4, LangGraph, RAG, MCP, multi-agent) with AI / LLM security embedded end to end.
Chief Technologist, Architect & Co-Founder
Co-founded and architected a Solana Web3 loyalty platform (tokenomics, NFT rewards, Apple / Google Wallet) and a multi-tenant Loyalty-as-a-Service SaaS at 99.9% uptime.
Solution Architect
Voice-AI ordering, conversational agents and RAG assistants for e-commerce and food-delivery clients; cut the release cycle from two weeks to two days.
Technology Consultant — Development & Architecture
Data-Platform-as-a-Service lakehouse (space-based architecture) plus Inventory- and Transport-as-a-Service platforms; distributed teams across four countries.
CTO & Software Architect
Owned the full technology strategy for a B2B marketplace scaling 0 → 10,000+ MAU; shipped five products; built and mentored a 15-engineer team.
Chief Technologist
Led the technology transformation scaling to 50,000+ daily orders across food, grocery, pharmacy and milk delivery with no service disruption.
Head of Technology Transformation
Technology transformation for a global mass-transit startup across three continents and seven cities; route & plan optimisers cut fleet cost 20%.
Technology Lead — Full-Stack (Data & AI)
Demand–supply prediction (LSTM / CNN), CNN facial recognition and fraud detection, deep-learning churn prediction, and the Careem BI / analytics portal used by 50+ global leaders.
Principal & Solution Architect / Head of Engineering
Led engineering for foodpanda.pk and eatoye.pk (Rocket Internet / Delivery Hero) with the Berlin global team; built an AI-powered BI and sales-recommender platform.
Principal Software Engineer
Architecture and delivery of ERP, CRM and e-commerce platforms for offshore clients across the US, Canada, UK and Australia.
Senior Software Developer & Architect
Delivered 50+ enterprise software projects (ERP, HRMS, finance, supply chain, CRM); led full-stack teams of 8–12.
Consulting & advisory: Daraz.pk (Alibaba Group) · Novo Nordisk · Zalingo (Australia) · OMEN Media (UK) · MAT Dubai · Cheezious · and 10+ international brands.
Principal AI & Enterprise Architect — hands-on from architecture to production, security included by default.
I’m Asif — a Principal AI & Enterprise Architect and fractional CTO with 20+ years designing, securing and scaling distributed and AI systems across healthtech, fintech, logistics, e-commerce and SaaS. 150+ software projects and 30+ AI platforms delivered as a hands-on architect and forward-deployed engineer — I embed with a team, design the system, and stay hands-on through delivery.
My focus is the security layer that makes agentic AI safe to run in production: threat modeling, NeMo Guardrails, AI security gateways, prompt-injection and jailbreak defense, red team and blue team — offensive and defensive — across LLM / RAG / agent / MCP surfaces, and governance aligned to the EU AI Act, ISO/IEC 42001 and NIST AI RMF.
As a fractional CTO and principal architect I own the whole picture: secure, scalable production architecture reviewed against ten pillars — scalability, security, AI security / guardrails, reliability & resilience, observability, governance, cost optimization, data & RAG security, performance and maintainability / DevSecOps.
Engagements
- Fractional / interim CTO
- Embedded principal AI architect
- AI security & red-team assessment
- Architecture review vs the 10 pillars
Focus
- AI security · red team & blue team
- Guardrails & AI security gateways
- Scalable agentic AI platforms
- Secure enterprise RAG
Education
- Ph.D. Computer Science (paused)
- M.S. Software Engineering — Gold Medallist
- B.S. Computer Science
One architect, the whole conversation
Most engagements need three different conversations — a business one, an architecture one, and an engineering one. I can have all three.
With the CEO / Founder
Define the business outcome — what "production-ready" actually needs to mean for this business, this risk tolerance and this budget.
With the CTO
Design the architecture — scalability, security, governance, observability, performance and cost, reviewed against the same ten pillars every time.
With the engineering team
Make it real — hands-on, forward-deployed, in the code and the infrastructure, not just in a slide deck handed off after the workshop.
Start with an outcome, not a contract
Five ways to begin — pick the one that matches where you are today.
AI Architecture Review
Find the production and architecture gaps in an existing AI/agent system — scored against all ten pillars, with a prioritized findings report.
AI Security Assessment
Identify and validate AI attack paths — authorized threat-modeling and testing of your LLMs, RAG, agents, tools and MCP surfaces, mapped to OWASP and MITRE ATLAS.
Production Readiness Assessment
Determine what stands between prototype and production — reliability, cost, observability and failure modes, not just a demo that worked once.
Fractional CTO
Senior AI and technology leadership without another full-time executive — strategy, architecture, security, vendor decisions and production readiness.
AI Transformation Advisory
Turn experimentation into an enterprise AI roadmap — technical due diligence, platform direction and a realistic path from pilot to production.
Questions worth answering up front
Who can architect secure enterprise AI systems?
I design and review enterprise AI and agentic AI architecture against ten pillars — scalability, security, AI security/guardrails, reliability, observability, governance, cost, data & RAG security, performance and DevSecOps — for clients in the GCC, Europe, Australia and the US.
Who can secure AI agents and prevent prompt injection or agent misuse?
I run authorized AI red-team and blue-team assessments — prompt-injection and jailbreak defense, agent and tool permissioning, MCP and A2A security — mapped to the OWASP Top 10 for LLM & Agentic Applications and MITRE ATLAS.
Who can review an enterprise RAG or agentic AI architecture before production?
I run AI Architecture Reviews and Production Readiness Assessments that check whether a RAG or agentic AI system respects data permissions, handles failure gracefully, and is observable and cost-controlled before it goes live.
Who can help prepare an AI system for the EU AI Act, NIST AI RMF or ISO/IEC 42001?
I advise on AI governance readiness — risk classification, model and data lineage, auditability and policy controls — aligned to the EU AI Act, NIST AI RMF and ISO/IEC 42001.
What does an AI Architecture Review actually include?
A structured review of your existing AI or agent system against scalability, security, governance, observability and cost — returned as a prioritized, evidence-backed findings report, not a generic checklist.
Do you work with startups, or only large enterprises?
Both — founders shipping their first AI product, enterprises adopting agentic AI, and companies that need fractional CTO or principal AI architect leadership without a full-time hire.
Building AI? Let's make sure it's ready for the real world.
Whether you need an architecture review, an AI security assessment, a production-readiness review or fractional CTO support — let's discuss the challenge. Remote engagements worldwide, prioritized for teams in the GCC, Europe, Australia and the US.