You will get Custom AI agents, RAG pipelines, Chatbots and Intelligent Automations
Project details
There's a wide gap between a chatbot that demos well and an AI system you can actually trust with real work. I build the latter.
On the agentic side, I design and build multi-agent workflows using LangChain and LangGraph where LLMs plan, delegate, and execute tasks autonomously, with proper guardrails so they don't go off the rails when inputs are messy or unexpected. RAG pipelines are built for accurate retrieval and grounded responses, not just semantic similarity that sounds plausible. Production chatbots get real memory management, context handling, and observability through LangSmith so you can trace exactly what happened when something doesn't behave as expected.
For automation, I use n8n, Zapier, and Make to connect your tools and eliminate the manual steps that slow your team down, lead qualification flows, AI-powered customer journeys, internal process automation. The goal is always systems that are robust enough to run without babysitting, and transparent enough that you can actually see what they're doing.
On the agentic side, I design and build multi-agent workflows using LangChain and LangGraph where LLMs plan, delegate, and execute tasks autonomously, with proper guardrails so they don't go off the rails when inputs are messy or unexpected. RAG pipelines are built for accurate retrieval and grounded responses, not just semantic similarity that sounds plausible. Production chatbots get real memory management, context handling, and observability through LangSmith so you can trace exactly what happened when something doesn't behave as expected.
For automation, I use n8n, Zapier, and Make to connect your tools and eliminate the manual steps that slow your team down, lead qualification flows, AI-powered customer journeys, internal process automation. The goal is always systems that are robust enough to run without babysitting, and transparent enough that you can actually see what they're doing.
AI Algorithms
Large Language ModelAI Applications
AI Chatbot, AI Content Creation, AI Mobile App Development, AI Text-to-Image, AI Text-to-Speech, AI-Enhanced Medical Imaging, AI-Generated Art, AI-Generated Music, AI-Generated Video, AIOps, Conversational AIAI Development Language
PythonAI Tools
Azure OpenAI, Bing AI, Copy.ai, GitHub Copilot, Gradio, Hugging Face, Jasper AI, Replit, StreamlitAI Models
ChatGPT, DALL-E, GPT-4, LLaMA, Midjourney AI, OpenAI Codex, Stable Diffusion, WhisperWhat's included $500
These options are included with the project scope.
$500
- Delivery Time 7 days
- 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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GR
Gabriel R.
Aug 5, 2025
Software Developer Needed for API & Web Scraping Tutorials
Very good freelancer. Serious, proactive. Highly recommended. Understands things quickly.
About Khuzaima
Full Stack & AI/ML Engineer | SaaS/MVP | AI Agents & Automation |
100%
Job Success
Lahore, Pakistan - 7:37 pm local time
I've been building at the intersection of software engineering and AI for years now, and the pattern that kills most AI projects isn't a bad idea, it's bad engineering underneath a good one. Models with no guardrails. Pipelines that assume clean data. Automations wired together with hope and duct tape. I build systems that don't have those problems, because I've seen what happens when they do.
What I bring that most people can't is the full picture. I'm not a data scientist who needs a separate dev team to ship anything, and I'm not an engineer who treats models like black boxes. I understand both sides, which means I can own an entire project, architecture, backend, frontend, AI layer, deployment, monitoring , without handing anything off or leaving gaps.
Following are the skills and expertise that I bring on the table:
🖥️ Full-Stack Development
I architect and build end-to-end web applications that are modular, testable, and built to grow. I care deeply about clean architecture, not just working code.
• Backend: Python, Django, FastAPI
• Frontend: React, Next.js, Node.js
• APIs: REST, GraphQL
• Databases: PostgreSQL, MongoDB, Redis
🤖 Agentic AI & LLM Applications
I build sophisticated AI systems that you can actually trust and trace. RAG pipelines with accurate retrieval and grounded responses, multi-agent workflows where LLMs plan, delegate, and execute tasks autonomously, and production-grade chatbots that actually understand context.
• Frameworks: LangChain, LangGraph, LangSmith
• Applications: RAG Pipelines, Multi-Agent Systems, Chatbots
• Capabilities: Observability, Evaluation, Guardrails, Memory Management
⚙️ Automation & Intelligent Workflows
I design automation systems that eliminate repetitive work and connect your tools into seamless pipelines. The goal is always the same: fewer manual steps, faster turnaround, zero dropped balls.
• Tools: n8n, Zapier, Make
• Communication: Twilio, SendGrid
• Use Cases: Lead Qualification, AI-Powered Customer Flows, Business Process Automation
📊 Data Science & Machine Learning
From raw, messy datasets to models that actually move metrics. I work across the full ML lifecycle, from problem framing and feature engineering all the way to evaluation and iteration. I know when to reach for a transformer and when a gradient-boosted tree is the smarter call.
• Domains: NLP, Computer Vision
• NLP: Text Classification, Summarization, Entity Recognition, Semantic Search
• CV: Object Detection, Segmentation, Image Classification
• Libraries: PyTorch, TensorFlow, Scikit-learn, HuggingFace
☁️ DevOps & MLOps
Application don't belong in Local Computers just like Models don't belong in notebooks, and I make sure they never remain there. I set up end-to-end Deloyment pipelines with tracking, versioning, automated retraining, and performance monitoring so your systems stay reliable long after the project wraps up.
• Cloud: AWS, GCP, Azure
• ML Platforms: MLflow, AWS SageMaker, GCP Vertex AI
• Infrastructure: Docker, Kubernetes, CI/CD Pipelines, Infrastructure-as-Code
What working with me looks like:
I treat every engagement as a technical partnership. Before writing code, I dig into the business context first, what problem are we really solving, what does success look like, and what could go wrong. I communicate proactively, document thoroughly, and never disappear mid-project.
If you're building something that matters and needs to be done right, whether it's a greenfield AI product, a legacy system that needs modernizing, or an ML model that needs to finally reach production, I'd love to hear about it.
Let's build something worth building.
Steps for completing your project
After purchasing the project, send requirements so Khuzaima can start the project.
Delivery time starts when Khuzaima receives requirements from you.
Khuzaima works on your project following the steps below.
Revisions may occur after the delivery date.
Proof of Concept (POC) and Detailed Plan
I will conduct a thorough Proof of Concept research and initial experimentation to lay out the best practices and frameworks/technologies that I will use for the development and finally provide a detailed layout development plan,
Core Development
Will develop the project as per the requirements and expectations.