Ahmed Afzal

AI Engineer II

Aug 2025 - Present • 9 mos

• Architected and delivered Medscribe, a production-grade clinical AI platform for real-time speech-to-text transcription and automated generation of structured, clinician-ready medical reports. • Designed and optimized multilingual Automatic Speech Recognition pipelines, handling accent variability, domain-specific medical terminology, and noisy clinical environments. • Implemented post-processing NLP pipelines for entity extraction, clinical summarization, and structured report generation aligned with healthcare documentation standards. • Integrated Electronic Health Records and Picture Archiving and Communication Systems via secure, standards-compliant APIs, ensuring interoperability with existing hospital information systems. • Enforced HIPAA-aligned security and compliance controls, including data encryption, access management, and audit logging across the entire data lifecycle. • Designed scalable backend architectures using containerized microservices to support high-throughput, low-latency AI inference workloads. • Led container orchestration strategies to ensure fault tolerance, horizontal scalability, and zero-downtime deployments in production environments. • Optimized system performance through resource profiling, concurrency tuning, and service-level optimizations, reducing inference latency and improving system responsiveness. • Implemented robust monitoring and logging mechanisms to track model performance, service health, and operational metrics in real time. • Collaborated with cross-functional teams to translate clinical requirements into production-ready AI solutions while maintaining regulatory compliance. • Contributed to architectural decisions involving service decoupling, API design, and deployment automation to improve maintainability and scalability. • Led iterative model evaluation cycles, incorporating feedback from medical professionals to improve usability, accuracy, and trustworthiness of AI outputs.

Associate AI Engineer

Nov 2024 - Aug 2025 • 9 mos

• Led the development of an AI-driven medical imaging platform leveraging the MERN stack and Python-based microservices, enabling modular, scalable system design for clinical-grade applications. • Designed and implemented interactive 2D and 3D DICOM viewers using Cornerstone, supporting advanced radiological workflows and real-time manipulation of large-scale medical imaging data. • Engineered high-performance rendering pipelines optimized for efficient handling of large DICOM datasets, ensuring low-latency visualization and smooth user interactions under clinical workloads. • Built and integrated computer vision pipelines for medical image preprocessing, feature extraction, and AI-assisted interpretation across multiple imaging modalities. • Fine-tuned large language models on healthcare-specific datasets for automated reporting on image modalities, incorporating tensor parallelism and pipeline parallelism to efficiently scale training and inference across multi-GPU environments. • Designed multimodal inference pipelines combining imaging features and structured clinical context to generate consistent, clinician-ready diagnostic summaries. • Developed secure, scalable RESTful APIs to support medical image retrieval, processing, and visualization across distributed systems and microservices. • Implemented role-based access control, encryption, and secure data handling mechanisms to protect sensitive medical data and maintain healthcare compliance standards.. • Authored detailed technical documentation covering system architecture, data flows, and deployment strategies to support long-term maintainability and team onboarding.