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I’m Salman, a data scientist and ML engineer based in Islamabad, Pakistan, focusing on building production-ready AI systems that scale in media and entertainment. I enjoy turning complex research into practical tools—LLM-powered RAG, vision pipelines, and multi-agent metadata mapping—delivering fast, reliable solutions across FastAPI, Docker, and cloud deployments.…

Salman Asad

Data Scientist, AI Engineer, AI Prompt Engineer, +3

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I’m Salman, a data scientist and ML engineer based in Islamabad, Pakistan, focusing on building production-ready AI systems that scale in media and entertainment. I enjoy turning complex research into practical tools—LLM-powered RAG, vision pipelines, and multi-agent metadata mapping—delivering fast, reliable solutions across FastAPI, Docker, and cloud deployments.…

Available to hire

I’m Salman, a data scientist and ML engineer based in Islamabad, Pakistan, focusing on building production-ready AI systems that scale in media and entertainment.

I enjoy turning complex research into practical tools—LLM-powered RAG, vision pipelines, and multi-agent metadata mapping—delivering fast, reliable solutions across FastAPI, Docker, and cloud deployments.

Skills

Experience Level

Expert

Expert

Expert

Expert

Expert

Expert

Intermediate

Intermediate

Language

English

Fluent

Work Experience

Data Scientist at Upright Music/CEBS

July 1, 2024 - Present

Evaluated and deployed DeepSeek-R1 LLM in production, comparing performance across VLLM, Ollama, Llama.cpp, and SGLang; selected Llama.cpp for production, optimizing GPU memory efficiency and inference speed. Designed and optimized an LLM-powered agentic RAG system for natural-language music search, deployed as a FastAPI endpoint. Improved Triton Inference Server configuration to reduce GPU usage by 30–40%, enabling multi-model deployment on shared infrastructure. Built a multi-agent system for mapping music metadata tags between taxonomies using LangGraph, FAISS, DeepSeek-R1, and prompt-controlled iterative routing with retry logic.

Computer Vision Engineer at xis.ai

October 1, 2023 - July 1, 2024

Converted and optimised models using ONNX and TensorRT, improving inference speed for real-time pipelines. Conducted research on anomaly-detection frameworks, SAHI Inferencing and Anomalib, for visual anomaly detection and quality inspection. Built high-performance detection systems (YOLOv10, RT-DETR) with focus on reliability and throughput— experience transferable to benchmarking LLM performance.

Machine Learning Intern at Strada Imaging

April 1, 2023 - September 1, 2023

Performed large-scale dataset preparation and annotation for segmentation tasks, ensuring high-quality training inputs. Applied classical CV methods for anomaly detection and feature extraction, contributing to model iteration cycles.