Machine Learning Engineer | SDE - 2

What will you do at Fynd

• 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.
• Responsible for the end-to-end deployment of predictive models including scoping, testing, implementation, maintenance, tracking, and optimization of predictive models.
• Responsible for complete and accurate documentation of all development around machine learning engineering.

Some specific requirements

• 4+ years of experience implementing and deploying machine learning and deep learning frameworks through distributions cluster and application programming in cloud platforms including AWS and GCP.
• Basic Knowledge of tools/libraries such as TensorFlow, PyTorch, Keras, NumPy, pandas etc.
• Strong understanding of data structures and algorithms and experience in Python. Some experience with computer vision(classification, segmentation, object detection) pipelines
• Deep experience across systems integration, information management, data management and architecture, and business analytics.
• Language: Python, Java, Scala, Unix bash script, REST API process.