Muhammad Bilal Khan

Senior Applied Machine Learning Engineer driving enterprise-scale AI deployments that optimize operational efficiency, enhance decision-making, and generate measurable ROI through multimodal, self-supervised, and generative AI systems. Orchestrates production-ready pipelines across vision-language modeling, NeRF-based 3D perception, SigLIP retrieval, diffusion-guided synthetic data, and long-context transformer architectures, delivering robust, scalable solutions for industrial, healthcare, and legal applications.

Architected 20+ cross-domain AI pipelines deployed across healthcare, industrial automation, and legal analytics, achieving industry-leading performance in anomaly detection, document QA, and multimodal retrieval. Leads high-impact initiatives translating frontier research into operational systems, ensuring precision, reliability, and explainability at scale.

Technically fluent in Python, PyTorch, TensorFlow, Transformers, ViTs, Whisper, BLIP-2, MiniGPT-4, and hybrid quantum-classical architectures. Executes enterprise-grade deployment through MLOps, CI/CD, distributed training, and cloud-native engineering (Azure ML, Google Cloud), integrating explainability frameworks, multimodal fusion, and scalable infrastructure (Docker, Kubernetes, CUDA).

Focused on advancing AI systems that are interpretable, resilient, and strategically transformative. Committed to translating frontier research into production-grade solutions that redefine industrial automation, intelligent decision-making, and cross-domain enterprise applications. Continuously drives applied innovation integrating multimodal reasoning, generative modeling, and hybrid quantum-classical architectures to deliver measurable operational impact and long-term strategic value.