You will get enterprise grade Multimodal RAG solutions with LangChain, LLMs & vector DBs
Project details
In today’s data-driven world, Retrieval-Augmented Generation (RAG) is the backbone of high-performance AI systems. With my expertise, I’ll build you a tailored RAG pipeline—whether text-only, multimodal (text + images + documents), or enterprise-grade with full MLOps/LLMOps integration. This ensures your system doesn’t just retrieve data but intelligently reasons over it with optimized embeddings and vector databases.
Unlike generic chatbots, my RAG solutions are production-grade and optimized for scalability, speed, and precision. From building pipelines with LangChain, LlamaIndex, or LangGraph to integrating FAISS, Pinecone, ChromaDB, or Weaviate, I ensure that your knowledge base is seamlessly connected to advanced LLMs (OpenAI, LLaMA, Hugging Face, etc.).
Clients choose me because I bring hands-on experience deploying 128+ ML/LLM models and building multimodal RAG pipelines that process 310K+ interactions monthly. Whether you want a “Chat with Your Documents” feature, GPU-optimized deployments, or a fully monitored RAG system, I’ll deliver with a focus on measurable ROI, reliability, and future-proof scaling.
Unlike generic chatbots, my RAG solutions are production-grade and optimized for scalability, speed, and precision. From building pipelines with LangChain, LlamaIndex, or LangGraph to integrating FAISS, Pinecone, ChromaDB, or Weaviate, I ensure that your knowledge base is seamlessly connected to advanced LLMs (OpenAI, LLaMA, Hugging Face, etc.).
Clients choose me because I bring hands-on experience deploying 128+ ML/LLM models and building multimodal RAG pipelines that process 310K+ interactions monthly. Whether you want a “Chat with Your Documents” feature, GPU-optimized deployments, or a fully monitored RAG system, I’ll deliver with a focus on measurable ROI, reliability, and future-proof scaling.
AI Algorithms
Autoencoder, Feedforward Neural Network, Gated Recurrent Unit, Generative Adversarial Network, Large Language Model, Long Short-Term Memory Network, Multimodal Large Language Model, Recurrent Neural Network, Transformer Model, Variational AutoencoderAI Applications
AI Chatbot, AI Content Creation, AI Text-to-Image, AI Text-to-Speech, AI-Enhanced Medical Imaging, AI-Generated Code, AI-Generated Video, AIOps, Conversational AI, Image Processing, Image-to-Image Translation, Natural Language GenerationAI Development Language
PythonAI Tools
Azure OpenAI, GitHub Copilot, Gradio, Hugging Face, Microsoft CNTK, NVIDIA AI Platform, PyTorch, Streamlit, TensorFlow, Word2vecAI Models
BERT, ChatGPT, DALL-E, GPT-3, GPT-4, GPT-J, GPT-Neo, LLaMA, Midjourney AI, OpenAI Codex, Stable Diffusion, WhisperWhat's included
| Service Tiers |
Starter
$500
|
Standard
$1,200
|
Advanced
$2,500
|
|---|---|---|---|
| Delivery Time | 7 days | 17 days | 20 days |
Number of Revisions | 1 | 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 |
Optional add-ons
You can add these on the next page.
Fast Delivery
+$100 - $500
Additional Revision
+$100
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JS
Joanne Da S.
Feb 27, 2024
Looking for C, MicroPython developer
AY
Alex Y.
Sep 18, 2023
Inverter
JS
Joanne Da S.
May 22, 2023
Looking for C, MicroPython developer
Great developer. Actually going to hire again right away
MP
Morgan P.
May 18, 2023
MicroPython Porting Engineer
Had an outstanding experience with this freelancer. He was fast, prompt, and effective, completely solving my problem immediately. Very very grateful to Zain.
Kd
Kasun d.
Nov 29, 2022
Conceptual System Level Paper Design of a Car-Mounted Solar Cell and Wind Generator charger
2nd time with him. Did a very good Job
About Zain
Senior AI Engineer | RAG | LangChain | AI Agents | Agentic AI | LLMOPs
100%
Job Success
Ubauro, Pakistan - 5:04 am local time
⭐️ 56+ completed projects spanning full-stack, machine learning, and generative AI.
⭐️ 16 end-to-end projects: RAG, multimodal RAG, Agentic AI systems, vision-language fine-tuning
Most AI projects stall in the gap between a working notebook and something you can actually ship and maintain — no clean deployment path, no way to evaluate or monitor it.
Closing that gap is my focus.
I'm Zain — a generative AI application engineer. Not just the model. The entire stack:
✅ Agent Orchestration ✅ RAG pipeline ✅ Fine-tuned LLM
✅API ✅ containerization and Kubernetes deployment ✅ CI/CD pipeline
✅ Monitoring and observability that keeps it all healthy at scale.
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WHAT I BUILD FOR YOU
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🔷 RAG & agents — semantic search, reranking, memory, and multi-agent workflows with LangChain, LangGraph, and CrewAI over FAISS, Qdrant, Pinecone, and AstraDB.
🔷 Fine-tuning — adapting open vision-language and language models (LoRA / QLoRA / PEFT / SFT) to your data and domain, efficiently enough to train on accessible hardware.
🔷 Deployment & MLOps — Docker, Kubernetes, FastAPI, GitHub Actions CI/CD, Terraform, and AWS, with Prometheus / Grafana monitoring and LLM-specific tracing via Langfuse. Your AI ships reliably, scales automatically, and is observable from day one.
🔷 Multimodal systems — pipelines that combine text, PDFs, images, and audio using OCR, vision-language models (CLIP, Qwen2-VL, Florence-2, MAIRA-2), and Whisper.
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WHO I WORK BEST WITH
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→ SMBs, funded startups, and agencies who need someone who can both build a GenAI feature and get it deployed — a builder who ships, not just a notebook.
→ CTOs and technical founders who need a senior AI partner, not just a developer
I do not take every project. I take projects where I can make a real difference — where the work is technically meaningful and the client is serious about building something that lasts.
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TECH STACK
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✅ Languages: Python · SQL · Shell · YAML · Rust
✅ LLMs & GenAI: OpenAI GPT · Claude · Gemini · LLaMA · Mistral · Hugging Face
✅ RAG & Agents: LangChain · LangGraph · LlamaIndex · Multimodal RAG
✅ Fine-Tuning: LoRA · QLoRA · SFT · DPO · RLHF
✅ Inference: vLLM · TGI · Quantization (GGUF · AWQ · GPTQ)
✅ Vector DBs: FAISS · ChromaDB · Pinecone · Weaviate. LanceDB
✅ ML/DL: PyTorch · TensorFlow · Scikit-Learn · Keras · OpenCV
✅ Backend: FastAPI · Flask · Django
✅ Databases: PostgreSQL · MySQL
✅ Cloud: AWS (EC2 · EKS · SageMaker · Fargate · ECR)
✅ Containers & Orchestration: Docker · Kubernetes · Helm
✅ Infrastructure as Code: Terraform · Ansible
✅ CI/CD: Jenkins · GitHub Actions · GitLab CI/CD · ArgoCD · CircleCI
✅ Monitoring: Prometheus · Grafana · ELK Stack · Langfuse
✅ Experiment Tracking: MLflow · Weights & Biases · DVC
✅ Version Control: Git · GitHub · GitLab
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If you are building an AI system that needs to actually work in production — not just in a notebook — I would like to hear about it.
Send me a message with what you are building, and I will tell you honestly whether I can help and how.
Steps for completing your project
After purchasing the project, send requirements so Zain can start the project.
Delivery time starts when Zain receives requirements from you.
Zain works on your project following the steps below.
Revisions may occur after the delivery date.
Requirement Analysis & Scoping
Analyze client goals, datasets, and use cases to define project scope.
Data Preparation & Embeddings
Preprocess data, select appropriate embedding models (OpenAI, LLaMA, etc.).