Ahmad Mujtaba

I’m an experienced Machine Learning Engineer with a strong background in AI-driven systems, predictive analytics, and LLM optimisation. I specialise in fine-tuning and efficiently adapting large models to deliver reliable NLP/LLM capabilities in real-world workflows.

In my current role, I integrate embedding-based, temporal, and NLP/LLM techniques into production-grade applications, including agentic systems with automation and chat interfaces. I use LoRA and PEFT to optimise model quality and maintain high performance through reproducible training pipelines.

I’m also hands-on with end-to-end deployment: I containerise and deploy models in cloud environments, maintain CI/CD pipelines, and build seamless RAG and real-time inference workflows. I work extensively with data engineering and feature engineering, using SQL and structured pipelines to support scalable ML training and live inference.

My background extends through data engineering experience (ETL/ELT pipelines and data preprocessing) and project work such as respiratory sound disease detection, an agent for movie review generation, and multi-objective optimisation for AMRs. I enjoy turning complex problems into dependable systems that align with user needs and business goals.