- Experience 3Years Responsibilities Building highly scalable ML based product pipeline and product platform Work with structured and unstructured data such as Images text using Computer vision NLP techniques Handle the deployment of ML models and awareness of optimised server configurations Processing cleansing and verifying the integrity of data used for analysis Develop custom data models and algorithms to apply to data sets Assess the effectiveness and accuracy of new data sources and data gathering techniques with different functional teams to implement models and monitor outcomes Develop processes and tools to monitor and analyse model performance and data accuracy on DataOps MLOps Requirements.
- Experience working with SAP Data Intelligence and building ML pipeline using ML Scenario Manager Ability to build pipeline for data pre-processing and feature extraction connecting to various data sources including structured unstructured and streaming data and RESTful API s Ability to create training pipelines and inference pipelines with python R and HANA PAL ML frameworks.
- Experience in setting up ML operations with respect to automated training and retraining process Ability to expose the ML models for consumption in external applications as well as through the batch scoring Should have exposure to SAP Data Intelligence Metadata Explorer ML Data Manager and integrated Jupyter lab.
- SAP Data Intelligence 4 to 6 years of experience
- Experience in Deploying the ML Scenarios to QA and Production environments using CI CD techniques
- Experience in using statistical computer languages R or Python to manipulate data and draw insights from large data sets Knowledge of a variety of machine learning techniques clustering decision tree learning artificial neural networks etc and their real world advantages drawbacks
- Experience working with either Computer Vision or NLP libraries such as OpenCV PyTorch Dlib CoreNLP SpaCy Gensim NLTK TextBlob Experience in deep learning including CNN training and testing inference deployment
- Experience in working with ML libraries such as Tensorflow scikit learn will be an added advantage Knowledge on dockers and Kubernetes Well versed with building custom operators based on custom docker containers Experience in GitHub Gitlab based version management.
- Knowledge on performing vertical and horizontal scaling in pipelines in Data Intelligence Excellent written and verbal communication skills for coordinating across teams Excellent Client facing communication skills A drive to learn and master new technologies and techniques