Work on the deployment of machine learning and deep learning models.
Help in accelerating model inference using various compression tools like onnx, Torch script, TensorRT, OpenVINO ....etc. Develop and maintain a data streaming pipeline (both batch and real-time) for data integration and large-scale machine learning.
Deliver best practices recommendations and technical presentations around machine learning deployment including real-time modeling.
Maintain and further enhance the internal model feature store and optimize the feature engineering script.
Full life cycle implementation from requirements analysis, platform selection, technical architecture design, application design and development, testing, and deployment.
Candidate Profile | Who Can Apply
ML Engineer with Hands on experience in Knowledge based graphs technology
Master’s degree in Computer Science, Mathematics or similar field
Experience in Knowledge based graphs technology and using reasoners. – ex: neo4j
Azure certification and Databricks certification
8+ years of experience in AI & DL methodology
AI Frameworks such as Tensorflow, Pytorch, and Keras