You will get an AI/LLM Architecture Audit and Prioritized Roadmap
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Project details
A senior, fixed-scope review of your AI system, whether it's RAG, agents, LLM serving, or cloud deployment. You get an honest assessment of architecture, cost, scalability, and risk, plus a prioritized roadmap. This is judgment, not hours.
I assess across deployment models, self-hosted open-source, managed APIs (Claude/OpenAI), and AWS (Bedrock/SageMaker), and tell you honestly which fits your cost and compliance needs. I'm not selling one stack, so the recommendation is unbiased.
PhD in Machine Learning, 15 years in AI, former CTO who has shipped production AI across multiple countries
I assess across deployment models, self-hosted open-source, managed APIs (Claude/OpenAI), and AWS (Bedrock/SageMaker), and tell you honestly which fits your cost and compliance needs. I'm not selling one stack, so the recommendation is unbiased.
PhD in Machine Learning, 15 years in AI, former CTO who has shipped production AI across multiple countries
Machine Learning Tools
Kubeflow, MATLAB, OpenCV, Python, PyTorch, TensorFlowWhat's included
| Service Tiers |
Starter
$500
|
Standard
$1,200
|
Advanced
$2,500
|
|---|---|---|---|
| Delivery Time | 5 days | 8 days | 12 days |
Number of Revisions | 1 | 2 | 2 |
Model Validation/Testing | |||
Model Documentation | |||
Data Source Connectivity | - | ||
Source Code | - | - |
Frequently asked questions
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SP
Stefano P.
Apr 25, 2026
AI Engineer for ELENA
Zeeshan did an excellent job on Phase 1 of the ELENA project.
This was not a simple task. ELENA required both technical execution and strategic understanding: backend, frontend, AI integration, document upload, RAG/document intelligence, demo flow, and a clear foundation for future development. Zeeshan handled the work professionally and showed strong ownership throughout the project.
What I appreciated most was that he did not just complete tasks mechanically. He understood the vision behind ELENA and helped shape the first working demo into something usable and presentable. He was clear in communication, detail-oriented, reliable, and always willing to explain technical decisions when needed.
He also went beyond the initial scope by helping bring forward a working document intelligence/RAG foundation earlier than expected, which added real value to the demo. The final result gives ELENA a solid base to continue into the next phase.
I also appreciated his technical feedback and strategic thinking on the broader AI direction of the project. It is rare to work with someone who can contribute both as an engineer and as a technical advisor.
Overall, this was a very positive experience. Zeeshan is professional, committed, technically strong, and trustworthy. I would be happy to work with him again in the future.
This was not a simple task. ELENA required both technical execution and strategic understanding: backend, frontend, AI integration, document upload, RAG/document intelligence, demo flow, and a clear foundation for future development. Zeeshan handled the work professionally and showed strong ownership throughout the project.
What I appreciated most was that he did not just complete tasks mechanically. He understood the vision behind ELENA and helped shape the first working demo into something usable and presentable. He was clear in communication, detail-oriented, reliable, and always willing to explain technical decisions when needed.
He also went beyond the initial scope by helping bring forward a working document intelligence/RAG foundation earlier than expected, which added real value to the demo. The final result gives ELENA a solid base to continue into the next phase.
I also appreciated his technical feedback and strategic thinking on the broader AI direction of the project. It is rare to work with someone who can contribute both as an engineer and as a technical advisor.
Overall, this was a very positive experience. Zeeshan is professional, committed, technically strong, and trustworthy. I would be happy to work with him again in the future.
IG
Imran G.
Mar 19, 2026
OpenClaw AI Assistant Setup with Ollama Local Models
Zeeshan did an excellent job setting up the OpenClaw AI assistant with Ollama local models. He was knowledgeable, responsive, and delivered everything as promised. Highly recommended and I would gladly work with him again.
TA
Toomah A.
Aug 24, 2022
Image classification model
Recommend to work with 👍
MJ
Mr J.
May 17, 2022
Object Tracking (Parcel) Using Deep Learning
Dr. Zeeshan Gillani is an expert in various fields, machine learning and deep learning for instance. He has a quite impressive educational background with a Ph.D. degree. He is responsive and efficient, will explain whatever you would need to know about the implementation. So happy to work with him, and will definitely add him to my upcoming projects.
GC
Guy C.
Sep 21, 2017
Window Covering
About zeeshan
AI Solutions Architect | LLM, Agentic AI, RAG & MLOps in Production
100%
Job Success
Lahore, Pakistan - 5:44 am local time
I am a PhD-level AI Systems Architect and Fractional CTO specializing in Large Language Models (LLMs), Agentic AI, Retrieval-Augmented Generation (RAG), Computer Vision, and production-grade MLOps pipelines.
𝐈 𝐝𝐨𝐧'𝐭 𝐣𝐮𝐬𝐭 𝐰𝐫𝐢𝐭𝐞 𝐜𝐨𝐝𝐞, 𝐈 𝐦𝐚𝐤𝐞 𝐚𝐫𝐜𝐡𝐢𝐭𝐞𝐜𝐭𝐮𝐫𝐞 𝐝𝐞𝐜𝐢𝐬𝐢𝐨𝐧𝐬 𝐭𝐡𝐚𝐭 𝐬𝐚𝐯𝐞 𝐜𝐨𝐦𝐩𝐚𝐧𝐢𝐞𝐬 𝐟𝐫𝐨𝐦 𝐞𝐱𝐩𝐞𝐧𝐬𝐢𝐯𝐞 𝐦𝐢𝐬𝐭𝐚𝐤𝐞𝐬 𝐝𝐨𝐰𝐧 𝐭𝐡𝐞 𝐫𝐨𝐚𝐝.
I help startups and enterprises design, build, and scale intelligent systems moving beyond demos into structured, reliable, and deployable AI architectures.
𝑴𝒚 𝑪𝒐𝒓𝒆 𝑬𝒙𝒑𝒆𝒓𝒕𝒊𝒔𝒆:
━━ 𝐀𝐈 𝐒𝐘𝐒𝐓𝐄𝐌𝐒 & 𝐋𝐋𝐌 𝐄𝐍𝐆𝐈𝐍𝐄𝐄𝐑𝐈𝐍𝐆 ━━
🅞 End-to-end RAG pipelines (hybrid retrieval, reranking, vector databases: FAISS, Pinecone, Weaviate)
🅞 Agentic AI systems with tool use, memory, and multi-agent orchestration
🅞 Fine-tuning and LoRA/QLoRA optimization pipelines
🅞 Scalable inference infrastructure and cloud-native ML systems (AWS)
🅞 CI/CD and MLOps workflows for production AI products
🅞 Workflow automation at production scale (n8n, self-hosted)
🅞 Scalable inference infrastructure and cloud-native ML systems on AWS
🅞 Frameworks: LangChain, LlamaIndex, FastAPI, Docker, Kubernetes
━━ 𝐂𝐎𝐌𝐏𝐔𝐓𝐄𝐑 𝐕𝐈𝐒𝐈𝐎𝐍 & 𝐆𝐄𝐍𝐄𝐑𝐀𝐓𝐈𝐕𝐄 𝐀𝐈 ━━
🅞 GAN architectures: StyleGAN, CycleGAN, pix2pix, SRGAN
🅞 Semantic segmentation: UNet, DeepLab, Mask R-CNN, custom 3D UNet
🅞 Object detection & tracking: YOLO, Faster R-CNN
🅞 Multi-GPU distributed training and CUDA optimization
🅞 Model evaluation, benchmarking, and deployment (cloud & on-prem)
🅞 PyTorch, TensorFlow, OpenCV
━━ 𝐅𝐑𝐀𝐂𝐓𝐈𝐎𝐍𝐀𝐋 𝐂𝐓𝐎 & 𝐓𝐄𝐂𝐇𝐍𝐈𝐂𝐀𝐋 𝐋𝐄𝐀𝐃𝐄𝐑𝐒𝐇𝐈𝐏 ━━
🅞 Define technical architecture and long-term technology roadmap
🅞 Design and scale AI/ML systems aligned with business goals
🅞 Build and lead engineering teams
🅞 Establish development processes and delivery frameworks
🅞 MLOps and cloud infrastructure strategy (AWS: EC2, S3, EKS, IAM)
🅞 Technical due diligence, vendor selection, and hiring
𝐖𝐇𝐎 𝐈 𝐖𝐎𝐑𝐊 𝐖𝐈𝐓𝐇
→ Funded startups needing senior AI leadership
→ Enterprises automating internal operations with AI
→ Founders who need a technical co-mind, not just a coder
𝐖𝐇𝐘 𝐂𝐋𝐈𝐄𝐍𝐓𝐒 𝐂𝐇𝐎𝐎𝐒𝐄 𝐌𝐄
✔ PhD in AI, I understand the science, not just the tools
✔ CTO background, I think in systems, not just features
✔ I've built and shipped, not just researched
✔ I work async, communicate clearly, deliver on time
✔ I tell you when something won't work, before you pay
for it
𝐓𝐄𝐂𝐇 𝐒𝐓𝐀𝐂𝐊
AI/ML → LangGraph, LangChain, OpenAI, Claude API, HuggingFace, RAG, Vector DBs
Automation → n8n, Webhook integrations, Event-driven pipelines
Backend → FastAPI, Python, REST APIs, PostgreSQL
Cloud → AWS (Solutions Architect), Docker, Linux servers
𝐄𝐍𝐆𝐀𝐆𝐄𝐌𝐄𝐍𝐓𝐒 𝐈 𝐎𝐅𝐅𝐄𝐑
- AI Automation Audit: find where AI saves you time/money
- MVP AI Pipeline Build: scoped, fixed price, delivered fast
- Fractional CTO: ongoing senior AI leadership
- Full System Build: end-to-end architecture + delivery
If you're building something serious with AI not a prototype that breaks in production, but a real system Let's talk.
Send me a message describing what you're trying to build.
Steps for completing your project
After purchasing the project, send requirements so zeeshan can start the project.
Delivery time starts when zeeshan receives requirements from you.
zeeshan works on your project following the steps below.
Revisions may occur after the delivery date.
Step 1: Discovery
I review your system access, briefing, and primary concern to scope the audit accurately.
Step 2: Audit & Analysis
Full review completed — written report and architecture diagrams prepared with prioritized findings.