Usamah Zaheer

• Designed and optimised ML infrastructure to analyse and enhance performance of models and systems on Arm architectures.
• Optimised ML compilers and libraries for Arm, improving inference performance and reducing latency.
• Conducted deep kernel-level analysis to identify and eliminate inference bottlenecks.
• Profiled and optimised runtime performance of ML models; developed scalable benchmarking solutions across cloud and edge environments.
• Built automated pipelines for data collection, preprocessing, and model evaluation, streamlining production workflows.
• Led cross-functional collaborations to align ML infrastructure with organisational goals, owning a new project from inception.

PyTorch · TensorFlow · JAX · FBGEMM · KleidiAI · ACL · ArmNN · OneDNN

• Developed CNN algorithms for segmentation, object detection, and classification, applying quantisation, pruning, and knowledge distillation for deployment on robot hardware.
• Architected evaluation tools and robotics algorithms for planning and navigation using C++. Conducted log analysis, debugging, and on-robot testing.
• Deployed models on diverse hardware and edge devices, optimising through profiling and bottleneck analysis using CUDA and cuDNN.
• Developed a VLM solution that saved over £100,000 and boosted productivity by 20x.
• Streamlined the ML lifecycle with model versioning, monitoring, and automated deployment using CI/CD pipelines.
• Presented complex ML projects to senior leaders and the CEO.

PyTorch · MXNet · ONNX · CUDA · cuDNN · C++

• Spearheaded development of an end-to-end automated ML pipeline for processing high-resolution data in real-time.
• Integrated cutting-edge CNNs in PyTorch and TensorFlow for high-resolution satellite imagery analysis.
• Led development of ML systems utilising Random Forest and SVM for predictive modelling.
• Deployed AI solutions in cloud environments with Docker and Kubernetes.

PyTorch · TensorFlow · Scikit-learn · Docker · Kubernetes · R

• Led design and implementation of AI agents for SDRs, integrating NLP and LLMs for accurate and scalable solutions.
• Managed cloud infrastructure on Vertex AI with Databricks and Snowflake, utilising RAG to enhance AI response precision.
• Orchestrated ML workflows and semantic search using LangChain and LlamaIndex.
• Oversaw containerised deployment with Docker and Kubernetes, implementing MLOps practices with MLflow.

ML Model Deployment App

• Developed an Android application for deploying ML models on edge devices for object detection using YOLO, Mask R-CNN, and SSD.
• Optimised using TensorFlow Lite with quantisation and pruning for low-latency, on-device inference.

360 Vision Navigation — Dyson

• Developed a robot utilising 26 sensors with SLAM technology and 360-degree vision for autonomous navigation.
• Advanced path planning algorithms and developed integration and unit tests in C++ and Python.

Air Purifiers Embedded Software — Dyson

• Contributed to embedded software for all Dyson air purifiers including Pure Cool using C and Python.
• Designed backbone logic behaviour system for hardware communication with FreeRTOS. Updated a fundamental library stack for 10+ projects.

AI4EO & CNN Research — University of Leicester

• Classified high-resolution satellite images for forest fire detection using CNNs, Random Forest, and SVM.
• Evaluated CNN architectures for autonomous vehicle applications, optimised with Transfer Learning and TensorRT.