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AI Research Engineer (Recommendation Project) at Huawei Telekomünikasyon Dış Ticaret Ltd. Key areas of responsibility will be:. . Make research on retrieval, pre-rank, and rank stages of SOTA Recommenders Systems,. . Design and build scalable ML services,. . Deploy ML services to production at scale considering resource constraints,. . Monitoring models to evaluate and improve services online,. . Play an active role in suggesting, collecting, and preprocessing the data necessary to train the ML models and evaluate performance,. . Consult with the other teams to determine the requirements and formalize the possible ML research directions.. . Essential technical requirements:. A. Basic computer science and programming languages. . Understanding of data structures, data modeling, and software architecture,. . Having expertise in object-oriented programming,. . Ability to write reusable and easily-maintainable code using beautiful and proper design patterns,. . Ability to write robust and optimized code in Python.. . Strong programming skills (Python, SQL, etc.) and experience with deep learning frameworks (e.g. TensorFlow, PyTorch, Keras). . Familiar with development processes (CI/CD, DevOps, MLOps). . B. Machine Learning. . Solid understanding of Neural Networks in theory such as convex optimization, hessian approximations, conjugate gradient, and Gauss-Newton steps,. . Familiarity with modern machine learning frameworks.. . C. Recommender System, related NLP and Computer Vision fields. . Proven experience as a Machine Learning/AI Engineer or similar role building largescale recommender systems to solve real live-stream problems,. . Practical experience in deploying and optimizing ML models in production,. . Experience in one of the fields: Deep Learning-based recommender models, NLP tasks (vector semantics such as TF-IDF or neural word embeds, entity labeling, text classification, etc.), Computer Vision tasks (such as Optical Character Recognition, Object Classification, Object detection, etc.). . D. Working efficiency. . Fully-easy working capability in version control systems such as Gitlab or Github,. . Experience in Docker for building a simulation of the production environment,. . Solid understanding of JSON file, and schema.. . E. Academic. Being published in Articles and Proceedings in reputable journals related to recommenders systems such as ACL and SIGIR is a significant plus.. Essential non-technical requirements:. . Fluent in English, both written and spoken,. . Ability to work in a multi-disciplinary and multi-cultural team.. . Company Location: Turkey.