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You will get AI System Architecture Design for Secure, Production-Ready Deployment
Project details
This project provides senior-level AI system architecture design for organizations that need secure, production-ready systems — not experiments or prototypes.
I design AI system architectures that operate reliably in real-world environments where security, compliance, scalability, and long-term operability matter. My work focuses on system boundaries, data flows, trust zones, AI component integration, and cloud deployment design, helping teams avoid costly architectural mistakes before implementation begins.
This engagement is ideal if you are planning to build or scale an AI-enabled system and want a clear, defensible architecture to build against. You'll receive a structured architecture tailored to your constraints, with practical guidance that engineering teams can confidently implement.
Deliverables typically include: a clear AI system architecture outlining key components and interactions, defined data flows and trust boundaries, deployment considerations, and a walkthrough explaining key design decisions.
This project does not include model training or application development. It is intentionally focused on architectural clarity, risk reduction, and production readiness.
I design AI system architectures that operate reliably in real-world environments where security, compliance, scalability, and long-term operability matter. My work focuses on system boundaries, data flows, trust zones, AI component integration, and cloud deployment design, helping teams avoid costly architectural mistakes before implementation begins.
This engagement is ideal if you are planning to build or scale an AI-enabled system and want a clear, defensible architecture to build against. You'll receive a structured architecture tailored to your constraints, with practical guidance that engineering teams can confidently implement.
Deliverables typically include: a clear AI system architecture outlining key components and interactions, defined data flows and trust boundaries, deployment considerations, and a walkthrough explaining key design decisions.
This project does not include model training or application development. It is intentionally focused on architectural clarity, risk reduction, and production readiness.
AI Development Type
Knowledge RepresentationAI Tools
MLflowAI Development Language
PythonWhat's included
| Service Tiers |
Starter
$600
|
Standard
$1,200
|
Advanced
$3,000
|
|---|---|---|---|
| Delivery Time | 3 days | 7 days | 14 days |
Number of Revisions | 1 | 2 | 3 |
AI Model Integration | - | - | - |
Detailed Code Comments | - | - | - |
Knowledge Graph | - | - | - |
Model Documentation | - | - | - |
Ontology | - | - | - |
Source Code | - | - | - |
Taxonomy | - | - | - |
Frequently asked questions
About Saurabh
AI Systems Architect | RAG, Agentic AI & Secure Enterprise Delivery
Mumbai, India - 7:54 am local time
I've worked as a forward-deployed AI architect for enterprise and government clients — including Google, IBM, HPE, Delhi State Government, Maharashtra State Government (including the Home Ministry), and Central Government agencies — owning AI-driven systems end-to-end: architecture, security, deployment, and the stakeholder alignment needed to actually ship in regulated environments. That work has included facial recognition systems, video/audio/document forensic workflows, a cybersecurity monitoring platform (threat detection, compliance automation, DLP) that cut manual threat-analysis time by 50%, an anti-fraud platform for Indian Railways that improved fraud prevention outcomes by 40%, and a citizen-facing AI chat platform for Dubai's RTA.
What I can do for you:
Take your AI POC to production — RAG pipelines, agentic workflows, LLM evaluation, the unglamorous work (auditability, monitoring, deployment hardening) that turns a working demo into something you can actually rely on.
Security & compliance-harden AI or software systems — SIEM/SOAR integration, DLP, vulnerability mitigation, and alignment with ISO 27001, PCI DSS, and GDPR — for teams that can't afford to treat this as an afterthought.
Full-stack build-out — Golang, Python, TypeScript/Node.js, React/Next.js, on AWS or GCP with Docker/Kubernetes, when you need one person who can own the whole stack rather than hand off between specialists.
Outside client work, I independently architected and shipped Project Kaizen, an AI sales-coaching platform that analyzes reps' real sales calls, diagnoses specific skill gaps, and generates targeted practice scenarios against an AI "customer" — built solo, end-to-end, and live in production. It's the clearest proof of how I work without a team behind me.
If you need someone who can sit with ambiguous requirements, make the hard architectural calls, and still be the one accountable when it goes live — that's the work I do. Happy to start with a scoped, low-risk piece of the problem if you want to see the fit before committing further.
Steps for completing your project
After purchasing the project, send requirements so Saurabh can start the project.
Delivery time starts when Saurabh receives requirements from you.
Saurabh works on your project following the steps below.
Revisions may occur after the delivery date.
Architecture context review
Review client inputs, constraints, and system context to align on goals and architectural scope.
System architecture design
Design the AI system architecture, including components, data flows, trust boundaries, and deployment considerations.