You will get a Custom Agentic RAG System for your Enterprise Data
Rising Talent
Rising Talent
Project details
I deliver high-accuracy, production-ready Generative AI solutions that turn your domain-specific data into a context-aware assistant. Having engineered RAG pipelines for HR, healthcare, and sales sectors, I ensure your AI provides precise responses while adhering to professional standards. My approach combines deep technical expertise with a focus on scalable, cloud-native deployment.
AI Algorithms
Feedforward Neural Network, Large Language Model, Multimodal Large Language Model, Transformer Model, YOLOAI Applications
AI Chatbot, AI Content Creation, Conversational AI, Natural Language Generation, Neural Machine TranslationAI Development Language
PythonAI Tools
Azure OpenAI, GitHub Copilot, Hugging Face, Microsoft 365 Copilot, PyTorch, Streamlit, TensorFlowAI Models
BERT, ChatGPT, LLaMA, OpenAI CodexWhat's included
| Service Tiers |
Starter
$100
|
Standard
$1,000
|
Advanced
$4,000
|
|---|---|---|---|
| Delivery Time | 3 days | 10 days | 21 days |
Number of Revisions | 0 | 3 | 5 |
AI Model Integration | - | - | |
Batch Normalization | - | - | - |
Database Integration | - | ||
Detailed Code Comments | - | ||
Image Upscaling | - | - | - |
MLOps | - | - | - |
Model Deployment | - | - | |
Model Documentation | - | - | |
Model Monitoring | - | - | - |
Model Testing & Optimization | - | - | - |
Model Tuning | - | - | |
Natural Language Processing | - | - | - |
NLP Tokenization | - | - | - |
Pre-Training | - | - | - |
Prompt Engineering | - | - | - |
Setup File | - | - | - |
Source Code | - |
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JU
Jennifer U.
Jun 1, 2026
AI Tech Integration (Growth/Entrepreneurial Mindset)
Syed was amazing.
WS
Wadih S.
Apr 24, 2026
Lead LLM Architect – Advanced RAG Pipelines, Knowledge Synthesis & API-Driven Orchestration
Working with Moonis was a fantastic experience. He took a highly complex AI architecture project and delivered a seamless, bug-free integration. He works incredibly fast, communicates perfectly, and clearly knows his stuff. Highly recommended for any complex software or integration projects.
AB
Asad B.
Apr 14, 2026
Senior AI Engineer – Agentic Systems & Multi-Agent Orchestration
I had a great experience working with Moonis. He is a true professional who delivers excellent quality work. His communication was clear and proactive, and he managed to deliver the final project well before the deadline. I would absolutely hire him again
About Syed Moonis
AI/ML Engineer | RAG, LLM Agents & Workflow Automation
100%
Job Success
Rawalpindi, Pakistan - 4:53 pm local time
🚀 Specialized in RAG, LLM Agents & Workflow Automation
📌 Active AI Freelancer Since 2022
⚡ Available Now | Fast Response Time
I am an AI/ML Engineer focused on building useful AI systems, not just demos. My work sits at the intersection of Generative AI, RAG architecture, LLM pipelines, workflow automation, data engineering, and production deployment.
I help clients turn scattered data, documents, transcripts, APIs, CRMs, spreadsheets, and dashboards into reliable AI-powered systems that save time, reduce manual work, and support real business operations.
Recent work includes a Zoom-to-Airtable n8n sales coaching dashboard, an AI Green Light PDF generator, a Google GenAI thumbnail image generator, advanced RAG pipelines, multi-agent knowledge systems, geospatial crop monitoring, route optimization, and anomaly detection systems.
• Build RAG systems that retrieve accurate answers from business documents and domain-specific data.
• Develop LLM agents that reason, call tools, process structured data, and support multi-step workflows.
• Automate processes using APIs, webhooks, n8n, Airtable, Google Docs, Slack, CRMs, and backend logic.
• Create dashboards and reporting systems for sales performance, call scoring, analytics, and decision-making.
• Deploy AI systems from prototype to production using Python, FastAPI, cloud infrastructure, Docker, and CI/CD.
➤ Generative AI, RAG & LLM Systems
• RAG Architecture: Designing retrieval pipelines for document search, knowledge retrieval, and enterprise Q&A.
• LLM Pipelines: Building custom AI workflows around OpenAI, Claude, Gemini, LangChain, LlamaIndex, and vector databases.
• Agentic AI Systems: Developing tool-using agents, multi-step reasoning flows, and automation-ready AI assistants.
• Prompt & Context Engineering: Improving output quality through retrieval logic, structured prompts, and response formatting.
• Enterprise Search: Turning internal documents, transcripts, reports, and domain knowledge into searchable AI systems.
➤ AI Workflow Automation & Integration
• Workflow Automation: Connecting AI models with business tools through n8n, APIs, webhooks, and backend services.
• Document Automation: Building transcript-to-report, PDF generation, Google Doc creation, and approval workflows.
• Sales & Ops Automation: Automating Zoom, Airtable, Slack, Google Drive, CRM, lead processing, and reporting workflows.
• Dashboard Automation: Feeding structured AI outputs into searchable dashboards, scorecards, and analytics interfaces.
• Reliability: Designing workflows with logging, validation, fallback paths, error handling, and clean handoff documentation.
➤ Machine Learning, Data & Predictive Intelligence
• Predictive Modeling: Building ML systems for forecasting, classification, decision support, and business intelligence.
• Anomaly Detection: Creating pipelines for identifying unusual behavior, outliers, and operational risks.
• Computer Vision & GIS: Developing image classification, satellite analysis, crop monitoring, and geospatial systems.
• Optimization Algorithms: Solving planning and routing problems using Genetic Algorithms and real-time constraints.
• Data Pipelines: Cleaning, structuring, transforming, and analyzing data for dashboards, models, and automated decisions.
➤ Cloud, Backend & Production Deployment
• Backend Engineering: Building Python, FastAPI, Flask, Node.js, and Express services for AI workflows and integrations.
• Full-Stack AI Tools: Creating React / Next.js interfaces for dashboards, internal tools, and AI applications.
• Cloud & DevOps: Deploying systems with AWS, Vercel, Docker, GitHub Actions, Linux, and production environments.
• Scalable Architecture: Designing AI systems that are maintainable, secure, modular, and easy to extend.
Technical Stack:
Python, JavaScript, TypeScript, SQL, FastAPI, Flask, Node.js, Express.js, React, Next.js, n8n, REST APIs, Webhooks, Airtable, Zoom, Slack, Google Docs, Google Drive, PostgreSQL, Neon, OpenAI, Claude, Gemini, Google GenAI, LangChain, LlamaIndex, LangGraph, RAG, LLM Agents, Vector Databases, Pinecone, Milvus, Qdrant, TensorFlow, PyTorch, Scikit-learn, Pandas, NumPy, OpenCV, AWS, Docker, GitHub Actions, Vercel, Linux.
Keywords:
AI/ML Engineer, AI Automation, Workflow Automation, RAG, Retrieval-Augmented Generation, LLM Agents, Agentic AI, AI Agent Development, n8n Automation, API Integration, Webhook Automation, Business Process Automation, AI Chatbot Development, Document Automation, PDF Automation, AI Dashboard, Sales Performance Dashboard, Google GenAI, AI Image Generation, Machine Learning, Predictive Analytics, Computer Vision, Data Pipelines, Cloud Deployment.
Steps for completing your project
After purchasing the project, send requirements so Syed Moonis can start the project.
Delivery time starts when Syed Moonis receives requirements from you.
Syed Moonis works on your project following the steps below.
Revisions may occur after the delivery date.
Discovery & Analysis
I review your data sources and define the specific goals for your AI assistant.
Pipeline Development
I build and fine-tune the LLM architecture and vector database for maximum retrieval accuracy.