20+ years building & securing systems 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.
# current focus role = "Principal AI & Enterprise Architect" also = "Fractional CTO · AI Security · Production-Grade AI" 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"
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.
Most of the risk in enterprise AI isn’t the model — it’s everything around it. Here’s where I come in.
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.
Authorized red-team assessments mapped to the OWASP Top 10 for LLM & Agentic Applications and MITRE ATLAS — evidence-backed findings, not a checklist.
Production-grade orchestration — reliability, retries, graceful degradation and cost controls built in from day one, not bolted on after the first incident.
End-to-end tracing, evaluation and audit trails — so every decision an agent makes is explainable after the fact, not a mystery.
Permission-aware retrieval, poisoning detection and citation verification — so answers respect the same access controls as the underlying documents.
Model routing, semantic caching and token optimization — so cost per task is known and controlled, not discovered on next month's invoice.
Every engagement maps to one of these six outcomes — the result you need, not a menu of unrelated services.
Turn a working prototype into an architecture that survives real production load, failure and change.
Threat-model and harden your LLMs, RAG, agents and MCP tools before an attacker — or your own agent — finds the gap.
Build the audit trail and control set that lets you say yes to AI without betting the company on it.
See what your agents actually do — before your customers or your board find out the hard way.
Know the cost per task, per user, per workflow — and route, cache and right-size until it stays that way.
Senior technical leadership for AI strategy and architecture — without a full-time executive hire.
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.
A systematic method for answering one question honestly: is this AI system actually ready for production? CTO-level architecture design and review — every system is held against the same ten pillars, from the first diagram to production sign-off. They run through every case study below.
Horizontal scale, stateless services, sharding, load-aware autoscaling.
Zero-trust, RBAC/ABAC, secrets management, least privilege, defense in depth.
NeMo Guardrails, prompt-injection & jailbreak defense, tool sandboxing, HITL.
Fallbacks, retries, circuit breakers, graceful degradation, DR.
OpenTelemetry traces, metrics, logs, evals and cost in one pane.
AI Act / ISO 42001 / NIST AI RMF, audit trails, model & data lineage.
Routing by cost, caching, right-sized models, budget guardrails.
Permission-aware retrieval, poisoning detection, citation verification, isolation.
Latency budgets, streaming, batching, semantic caching, profiling.
IaC, CI/CD, SBOM & supply-chain scanning, typed contracts, tests.
Prioritized for teams in the GCC, Europe, Australia and the US — remote engagements worldwide.
You've shipped the first version. Now investors, customers or auditors are asking whether it's actually production-ready.
You're moving from single LLM calls to autonomous, tool-using agents — and the blast radius just got a lot bigger.
Your AI is live, and nobody outside your own team has ever tried to break it.
Legal and compliance are asking questions about your AI systems that engineering can't yet answer.
Your AI spend is climbing faster than usage, and nobody can tell you why.
You need principal-level AI and architecture judgment — without a full-time executive headcount.
Independent architecture and product work delivered through Spire Digi Solution (RetailGPT with Datacue — Sanabil Venture) — implementation-ready blueprints and shipped systems.
A controlled environment for finding out how your agents, RAG and MCP tools actually fail — before an attacker does.
500 structured security scenarios across 20 AI-specific domains, run by a fleet of specialized security agents.
One command center that correlates AI-native attack paths with conventional cyber findings — not two disconnected tools.
The production backbone for building, orchestrating and operating enterprise AI agents — not another agent demo.
One control plane for every LLM call in the org — routing, cost, reliability and security in one place.
RAG that respects permissions, cites its sources, and doesn’t get poisoned by a bad document.
Because “it felt fine in testing” isn’t a release process for a probabilistic system.
Ask retail data a question in plain English and get a governed, cited, multi-format answer back — safely.
Principal architect, CTO and forward-deployed engineer — owning solution architecture, AI / security strategy and end-to-end delivery.
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+.
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.
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.
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.
Data-Platform-as-a-Service lakehouse (space-based architecture) plus Inventory- and Transport-as-a-Service platforms; distributed teams across four countries.
Owned the full technology strategy for a B2B marketplace scaling 0 → 10,000+ MAU; shipped five products; built and mentored a 15-engineer team.
Led the technology transformation scaling to 50,000+ daily orders across food, grocery, pharmacy and milk delivery with no service disruption.
Technology transformation for a global mass-transit startup across three continents and seven cities; route & plan optimisers cut fleet cost 20%.
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.
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.
Architecture and delivery of ERP, CRM and e-commerce platforms for offshore clients across the US, Canada, UK and Australia.
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.
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.
Most engagements need three different conversations — a business one, an architecture one, and an engineering one. I can have all three.
Define the business outcome — what "production-ready" actually needs to mean for this business, this risk tolerance and this budget.
Design the architecture — scalability, security, governance, observability, performance and cost, reviewed against the same ten pillars every time.
Make it real — hands-on, forward-deployed, in the code and the infrastructure, not just in a slide deck handed off after the workshop.
Four ways to begin — pick the one that matches where you are today.
A structured review of your existing AI/agent architecture against all ten pillars — scalability, security, governance, observability and cost — with a prioritized findings report.
Authorized threat-modeling and testing of your LLMs, RAG, agents, tools and MCP surfaces, mapped to the OWASP LLM & Agentic Top 10 and MITRE ATLAS.
A go / no-go verdict on whether an AI system is actually ready for production — reliability, cost, observability and failure modes, not just a demo that worked once.
Ongoing architecture and technology leadership for AI strategy and transformation — embedded, senior, and accountable for the outcome, not just advice.
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.
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.
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.
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.
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.
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.
Available for remote engagements worldwide — AI architecture reviews, AI security assessments and fractional CTO work — prioritized for teams in the GCC, Europe, Australia and the US.