We are looking for a "AI Research Engineer" specified in Recommendation Project for our global business partner runs in Telecommunication Industry.

Description

Key areas of responsibility will be:

• Make research on retrieval, pre-rank, and rank stages of 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.

Requirements

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.