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I will be llm engineer ai chatbots, rag , ai agents python and langchain
Arslan Ali
Informazioni su questo servizio
I'm a Pakistan-based Generative AI/ML & LLM Engineer with 4+ years building AI chatbots, RAG systems, and LLM SaaS platforms end to end, from backend to deployed frontend.
I ground LLMs in your own data (PDFs, databases, APIs) so responses are accurate instead of hallucinated, and I build multi-step AI agents that can search, calculate, and take real actions using Python, LangChain, and LlamaIndex.
What you get:
- Custom RAG systems built on your documents or database, with vector search (Pinecone, Chroma, Weaviate)
- AI agents with tool-use browsing, calculations, API calls, multi-step reasoning
- Full-stack AI applications, from FastAPI backend to deployed frontend
- Prompt engineering and fine-tuning for better accuracy and lower token cost
- Secure API endpoints and cloud deployment (Docker, AWS, GCP)
Why work with me:
- Clean, production-grade Python and LLM code not notebook prototypes
- Deep hands-on experience with LangChain and LlamaIndex
- Clear communication and on-time delivery
Send me your use case and I'll tell you honestly whether RAG, a fine-tune, or a simple prompt fix is the right call for your problem before you spend a dollar on the wrong solution
Scopri di più su Arslan Ali
Arslan Ali
AI Engineer
- DaPakistan
- Membro dagiu 2024
- Tempo di risposta medio1 ora
- Ultima consegna10 mesi
Lingue
Urdu, Tedesco, Francese, Inglese
I'm an AI Engineer with 3+ years of experience building production-ready AI solutions for startups and businesses. I specialize in AI SaaS applications, LLM-powered assistants, RAG systems, AI agents, and workflow automation. I develop scalable backends with FastAPI, modern React.js frontends, and intelligent systems that process text, images, and voice. From enterprise chatbots to autonomous multi-agent platforms, I deliver reliable, secure, and production-ready AI products that solve real business problems.
FAQ
Why does my business need an AI chatbot instead of regular live chat support?
Live chat needs a human online to work. A custom AI chatbot doesn't — it answers customer questions, qualifies leads, and handles orders around the clock. You keep converting visitors into customers even when your team is offline
Will the chatbot actually understand my business, or is it a generic GPT wrapper?
It's fully custom, not generic. I train the chatbot on your specific data — products, FAQs, policies, and tone of voice — so it responds like someone who works at your company, not a copy-pasted GPT template
Why use LangChain instead of just calling the OpenAI API directly?
LangChain adds memory, structured prompts, and live connections to your own data — documents, databases, or APIs — on top of the raw OpenAI API. That's what keeps answers accurate and consistent instead of generic, hallucination-prone GPT output.
Where can this AI chatbot be deployed?
Wherever your customers already are — your website, WhatsApp, Messenger, or as an internal tool for your team. Every integration is tested in your live environment before handover, not just demoed in isolation.
How long does it take to build and deliver a custom AI chatbot?
Multi-platform builds or ones needing deeper data integration (CRM, large document sets) can run longer. I confirm an exact timeline after reviewing your requirements, not before.
Do you provide the source code and deployment support after delivery?
Yes, full source code is included along with deployment support on AWS, Azure, GCP, or your own server
How does n8n fit into this — what does it automate?
n8n powers the automation behind your chatbot or voice agent — saving leads to your CRM, sending alerts for hot leads, booking appointments, or triggering follow-ups. The AI handles the conversation; n8n handles what happens next, automatically.
Is the voice agent built with Vapi, and what does that mean for call quality/latency?
Yes, Vapi powers the voice infrastructure — built for real-time voice AI with low latency and natural-sounding speech, so calls feel human, not robotic. Paired with LLM understanding and LangChain accuracy, it's designed for smooth, responsive real-world calls
