Senior R&D Engineer

- Education: Bachelor's or Master's degree in Computer Science, Data Science, Machine Learning, or a related field.
- Experience: A minimum of 2 years of experience in Machine Learning or AI.
- Programming Skills: Proficiency in Python. Experience with R or C++ is a plus.
- Technical Expertise: Experience in image processing, text processing, preprocessing techniques, and neural networks. Extensive knowledge of architectures like CNN, RNN, GraphNN, YOLO, Transformer, BERT, T5, and their variants. Experience in natural language processing, information extraction, and object detection.
- Mathematical Foundation: A strong foundation in mathematics, particularly in areas like probability, statistics, linear algebra, and optimization.
- Data Flexibility: Flexibility in data usage and optimization to maximize the effectiveness of AI/ML models.
- Project Experience: Preference for candidates who have participated in large-scale projects involving image analysis, Natural language processing, Objects detection, Objects recognitions, OCR and the application of Large Language Models (LLMs), RAG.
- Model Development: Ability to independently develop, innovate, or optimize models ranging from simple to complex.
- Source Control and CI/CD: Experience with Git/GitLab and setting up CI/CD systems.
- Containerization and MLOps: Experience with Docker and MLOps frameworks such as MLflow and ClearML to manage and deploy AI/ML models effectively.
- Model Deployment: Experience with model quantization and conversion to ONNX Runtime, TFLite, TensorRT to optimize deployment.
- Service Development: Experience with messaging and data processing systems like RabbitMQ, Apache Kafka, or SparkML.
- Additional Preferences: Candidates with experience in building and deploying MLOps systems, Data Warehouses, Data Lakes, and those who have contributed to AI/ML research are highly preferred.
- Academic Contributions: Preference will be given to candidates who have made significant contributions to AI/ML research, including scientific publications.
- Supplementary Skills: Creative thinking, quick learning ability, and the capacity to apply new techniques in AI/ML. Strong communication skills and effectiveness in crossdisciplinary collaboration.