Applied machine learning & computer vision
Builds practical visual AI workflows across OCR, image processing, classification, and object detection, connecting model capability to usable software.
MuFaw leadership
Laiba Shahid
Co-founder, Director & Lead AI Engineer at MuFaw
Laiba Shahid is an AI engineer and sustainable AI researcher focused on practical machine learning, computer vision, AI systems, and responsible deployment that creates useful real-world outcomes.
Sustainable AI, computer vision, and practical AI systems
Professional profile
Building practical AI with sustainability in mind
At MuFaw, Laiba leads AI product design and engineering across machine learning, computer vision, LLM applications, automation, and scalable deployment. Her work treats AI as a complete system: models, APIs, interfaces, infrastructure, and the needs of the people who rely on it.
Her research direction centres on sustainable and trustworthy AI. She investigates how teams can make informed engineering choices by evaluating resource use and lifecycle impact alongside quality, speed, and practical usefulness.
Expertise
Technical focus areas
AI products & LLM systems
Designs AI-enabled products that combine language models, automation, APIs, interfaces, and user-focused workflows for real delivery contexts.
Cloud deployment & AI operations
Works across containerized services, cloud infrastructure, and production-oriented delivery to help AI systems move beyond experiments.
Research & leadership
Engineering technology for meaningful outcomes
Sustainable and trustworthy AI research
Explores practical ways to evaluate AI systems by their usefulness, resource consumption, and lifecycle impact alongside accuracy and latency.
Resource-efficient AI design
Investigates system and model-design choices that reduce unnecessary computation and token use while preserving meaningful performance.
Technical leadership & mentoring
Supports teams through AI product design, technical collaboration, mentoring, workshops, and engineering practices grounded in practical responsibility.
Human-centred technology
Brings a social-impact perspective to AI engineering by focusing on technology that responds to real needs and creates useful public value.
Build with MuFaw