Role Description Mutual Of Omaha is seeking an experienced developer to lead the design and implementation of Machine Learning and generative AI applications for our Workplace Solutions division. This role calls for a multifaceted expert with deep knowledge of Machine Learning, Generative AI, and DevOps who can seamlessly align technical solutions with business needs. You will champion strategic initiatives that leverage analytics and artificial intelligence to enhance customer experiences, optimize operations, and deliver business value through scalable, ethical technology solutions. This role is essential in shaping our AI strategy and governance framework, enabling intelligent automation, machine learning operations, and integrated insights. You will drive this transformation by operationalizing AI to unlock actionable intelligence across the organization. We are looking for someone who can rapidly advance our GenAI capability. You’ll bring seasoned critical thinking, mentor others, and act as a strong communicator with the business to drive adoption and impact. Qualifications - Bachelor’s degree in Science, Engineering, or quantitative fields (Mathematics, Statistics, Data Science, etc.), or equivalent experience. - At least 5 years deploying and supporting full-stack applications in enterprise environments. - Several years of experience integrating AI, ML solutions into full-stack applications. - Experienced in full-stack development (backend, frontend, and database technologies). - Expertise in frameworks like React or Vue. - Strong skills in Python, TypeScript, git, SQL, CI/CD pipelines, automated testing, and DevOps best practices. - Experienced in AWS services, particularly Bedrock, SageMaker, S3, Lambda, and infrastructure as code (CDK). - Extensive experience applying software engineering design patterns and enterprise application architecture principles. - Experienced in developing and deploying AI/ML systems. - Expertise in GenAI application development, LLM integration, and AIOps. - Strong communicator and collaborator. - Resilient and resourceful problem solver. - Comfortable with ambiguity and change, demonstrating high learning agility. - Promotes a culture of diversity and inclusion. - Able to work remotely with access to a high-speed internet connection and located in the United States or Puerto Rico. Requirements - Group Benefits or insurance industry experience (preferred). - Advanced degree in an analytical field with proven in-depth AI knowledge (preferred). - Experienced with AI/ML OPS practices, distributed data processing, and building real-time data pipelines (preferred). - Familiarity with single or multi-cloud agentic architecture for building LLM-based applications (preferred). - AI engineer who can design and implement production-grade solutions across probabilistic and deterministic approaches (preferred). Benefits - Estimated Salary (Levels have variable responsibilities and qualifications): - Engineer II: $110,000 - $130,000, plus annual bonus - Engineer III: $130,000 - $150,000, plus annual bonus - 401(k) plan with a 2% company contribution and 6% company match - Work-life balance with vacation, personal time, and paid holidays.

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.