AI Engineer with 3+ years of experience, specialising in DL systems in high-stakes environments.
Hands-on experience in moving research prototypes to reliable systems, most notably a medical diagnostic endto-end AI pipeline with hybrid deployment across edge, AWS, and GCP to serve 4500+ patients in hospital
production environments (92% accuracy approaching cardiologist-level performance, EU AI Act compliant).
Technical depth: computer vision for medical imaging (CNNs and RNNs for segmentation, object detection;
diffusion models for synthetic medical image generation), NLP with LLM integration (fine-tuning, retrieval systems,
Dynamic RAG for a 40% cost reduction through prompt optimisation), MLOps infrastructure (CI/CD, multi-cloud
AWS/GCP, edge deployment achieving sub-100ms latency). Proven ability in handling real-world complexity:
distribution shift, legacy system integration, regulatory compliance (GDPR, EU AI Act). Interdisciplinary training
enabling stakeholder translation and team coordination; findings presented at a national healthcare conference.
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