RAG & LLM Applications
Chatbots and knowledge assistants grounded in your documents and databases, with citations, evaluation and guardrails.
Open to remote roles · full-time, contract & freelance
AI/ML Engineer with 4+ years designing, training and deploying RAG systems, AI agents, computer vision and generative models, from PyTorch experiments to FastAPI services in production.
About
I'm an AI/ML Engineer at Arhamsoft (Pvt) Ltd in Lahore, where since 2022 I've led end-to-end delivery of AI products: object detection, OCR pipelines, speech-to-video and image-to-video generation, and retrieval-augmented generation and multi-agent systems that automate real business workflows.
I enjoy the whole lifecycle: reading the research, training and evaluating models in PyTorch or TensorFlow, then turning them into reliable, cost-efficient services with FastAPI, Docker and the cloud. I care about measurable results, clean code and systems that keep working after the demo.
What I build
Available for full-time remote roles, contracts and freelance projects.
Chatbots and knowledge assistants grounded in your documents and databases, with citations, evaluation and guardrails.
Tool-using agents and multi-agent workflows with LangGraph, CrewAI and n8n, including human-in-the-loop approvals.
Detection, tracking, counting and classification with YOLO, OpenCV and PyTorch, from webcam to CCTV streams.
Turn invoices, receipts and scanned PDFs into validated structured data with OCR and LLM extraction.
Real-time voice agents with speech-to-text, LLM reasoning, tool calling and natural text-to-speech.
Diffusion and VAE pipelines, MCP servers and LLM integrations, shipped as Dockerised APIs on AWS, GCP or Azure.
Portfolio
Open-source flagships with tests and docs, plus work from client and university projects.
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Experience
Arhamsoft (Pvt) Ltd · Lahore, Pakistan
University of Management and Technology · Lahore
Benchmarked frameworks for converting complex PDF and Excel tables into structured data; LlamaIndex proved the most robust for parsing and contextual extraction.
Compared pose-estimation datasets and deep learning architectures for production-level ASL recognition, reaching 99.5%+ accuracy on digits and alphabets.
Evaluated high-dimensional transformation methods and identified MST++ as the best approach for converting 3-channel RGB images into compact 31-band representations.
Toolbox
Contact
Hiring for a remote AI role or need an AI project delivered? Send a message and I usually reply within a day.

AI answers can be imperfect. For anything important, email Muhammad directly.