Sr Deep Learning Engineer

Job Description

Experience Required - 2-5 years Skillset - Must Have Languages & Frameworks: - Python (Pandas, NumPy, Sklearn, TensorFlow, Keras, Pytorch, Tkinter, Selenium, Beautiful soup, regular expressions, Flask), R Programming, MySQL, Analytics, Data Crawling, Flask API, Statistical Modelling, Visualization. Proficient in a t least a few of the following: Regression, Bayesian methods, Tree-based learners, SVM, RF, XgBoost, Time Series Modeling, Clustering Problem-solving: Ability to break the problem into small parts and apply relevant techniques to drive required outcomes. Experience with classical ML algorithms and its underline mathematics for data patterns and profiling. Experience working with Hyper-parameter optimisation, feature engineering, feature selection, calibration of models and benchmarking performance via AUC/Gini, Capture rates, K-S and other relevant metrics Should be strong in Data Structures and Algorithms. Should be able to do code complexity analysis/optimisation. Should be able to read research papers and pick ideas to quickly reproduce research in the most comfortable Deep Learning library. Good Verbal and Written communication skills Good To Have Exposure to Production Deployment of ML models Exposure to Deep learning technologies like Computer Vision and NLP. Exposure to Cloud Technologies like AWS or Azure Cloud. Exposure to deployment tools like Docker and Kubernetes Exposure to Big Data Technologies like Hadoop, Spark, Hive etc. Responsibilities - To provide expertise on multiple phases of a ML Project/research lifecycle from design, implementation, optimisation and support. Analyse real world problems and identify AI and ML solutions Explore and visualise data to gain an understanding, and then identify differences in data distribution that could affect performance in the real world Verifying data quality, and/or ensuring it via data cleaning Find available online datasets that could be used for training Generate Synthetic data and augmentation pipelines Define validation strategies Train and retrain Models and tune their hyper parameters wherever required Fine tune pertained models via transfer learning. Design & Develop high-quality products using python, ML Libraries Show more Show less