Role Description Senior AI Engineer olarak, TRAICK’in medikal görüntüleme AI modellerinin geliştirilmesi, iyileştirilmesi ve üretim ortamına alınmasında aktif rol alacaksınız. - Hem araştırma hem ürün geliştirme içerir - Model geliştirmeden deployment’a kadar uçtan uca sorumluluk içerir - Klinik gerçeklik ile teknik performans arasında denge kurmayı gerektirir Responsibilities - Medikal görüntü işleme modelleri geliştirmek ve optimize etmek - Model performansını artırmak (F1, AUC, sensitivity, specificity vb.) - Veri pipeline, preprocessing ve augmentation süreçlerini geliştirmek - Model training, evaluation ve versiyonlama süreçlerini yönetmek - MLOps süreçlerini kurmak ve iyileştirmek - AI modellerini production ortamına almak ve izlemek - Klinik ekip ile birlikte model çıktılarının yorumlanabilirliğini artırmak Qualifications - Bilgisayar Mühendisliği, Yapay Zekâ, Biyomedikal Mühendislik veya ilgili alanlarda lisans derecesi (Yüksek lisans / doktora tercih sebebidir) - 7+ yıl AI / Machine Learning / Computer Vision alanında kanıtlanmış deneyim - Video tabanlı medikal görüntü analizi deneyimi - Python, PyTorch veya TensorFlow tecrübesi - Model geliştirme, eğitim ve değerlendirme süreçlerine hakimiyet - Veri işleme ve model optimizasyonu konusunda deneyim - Git, experiment tracking araçları (Comet, MLflow vb.) ve CI/CD süreçlerine hakimiyet - ML pipeline, veri seti yönetimi ve performans metrikleri konusunda deneyim - Büyük ölçekli veri pipeline’ları, class imbalance yönetimi ve metrik odaklı model eğitimi deneyimi - Hızlı iterasyon ve problem çözme yetkinliği - Takım içinde efektif iletişim kurabilme - Türkçe ve İngilizceyi akıcı şekilde kullanabilme Bonus - Medikal görüntüleme yazılımı deneyimi - Segmentation, detection veya classification projelerinde deneyim - MLOps, CI/CD, Docker, Kubernetes tecrübesi - DICOM, PACS, HL7/FHIR, EHR bilgisi - Sağlık regülasyonları (ör. European Union Medical Device Regulation) hakkında bilgi

• Own the enterprise AI platform, including Microsoft AI ecosystem (Azure AI, Copilot, Power Platform) • Manage platform architecture, environments, and integrations • Own licensing, cost management, and vendor relationships • Lead delivery of AI and automation solutions across all business units • Establish a repeatable delivery model for AI and automation initiatives • Lead enterprise AI training and enablement programs

• Develop and deploy at least three (3) production-grade AI/ML models, including use cases such as predictive maintenance, anomaly detection, classification, forecasting, or optimization. • Design, build, and maintain end-to-end machine learning pipelines, including data preprocessing, feature engineering, model training, evaluation, and deployment. • Apply deep expertise in PyTorch and/or TensorFlow to develop and fine-tune advanced machine learning and deep learning models. • Implement and support scalable model serving architectures, ensuring high availability, low latency, and secure inference in production environments. • Collaborate with data engineers to access, transform, and prepare large-scale datasets for model training and inference. • Partner with product owners, analysts, and stakeholders to translate business requirements into machine learning solutions. • Monitor model performance in production, including drift detection, accuracy tracking, and retraining strategies. • Ensure compliance with federal security, privacy, and governance standards in all AI/ML implementations. • Participate in Agile development cycles, including sprint planning, design reviews, and technical demonstrations. • Document model architectures, training methodologies, and deployment processes for maintainability and auditability.

• Translate ambiguous customer questions into well-scoped data science projects spanning panel data, time series, and causal inference• Engineer features from large alternative-data panels (transaction-level, invoice-level, web-scraped) using Spark• Build, validate, and interpret causal and predictive models that link alternative-data signals to financial outcomes such as revenue, earnings surprise, and KPI inflections• Author technical white papers and customer analyses for institutional investors and Fortune 500 readers, including figures, equations, and narrative framing for sophisticated readers• Use LLM coding assistants and agents as a primary collaborator: prototype faster, write higher-quality code, audit your own work, and ship deliverables in compressed timelines• Build internal LLM-driven tooling (agents, eval harnesses, retrieval pipelines) for the broader organization• Partner with data engineering, product, and revenue teams to close the loop between signal development and the customer-facing product• Set technical standards for the team: PEP 8, type hints, vectorized operations, reproducible notebooks, sound methodology, and citation discipline