Machine Learning Engineer at CodeMantra

Job Description

Codemantra uses technology, automation, process management and deep industry experience to design innovative solutions that improve the competitive position of our publishing partners. Simply put, we give you an edge.

Skills and Qualifications

• Strong Hands-on experience in Python Programming
• Working experience with Computer Vision models - Object Detection Model, Image Classification
• Good experience in feature extraction, feature selection techniques and transfer learning
• Working Experience in building deep learning NLP Models for text classification, image analytics-CNN,RNN,LSTM.
• Working Experience in any of the AWS/GCP cloud platforms, exposure in fetching data from various sources.
• Good experience in exploratory data analysis, data visualisation, and other data preprocessing techniques.
• Knowledge in any one of the DL frameworks like Tensorflow, Pytorch, Keras, Caffe Good knowledge in statistics,distribution of data and in supervised and unsupervised machine learning algorithms.
• Exposure to OpenCV Familiarity with GPUs + CUDA Experience with NVIDIA software for cluster management and provisioning such as nvsm, dcgm and DeepOps.
• We are looking for a candidate with 3+ years of relevant experience , who has attained a Graduate degree in Computer Science, Statistics, Informatics, Information Systems or another quantitative field. They should also have experience using the following software/tools: *Experience with big data tools: Hadoop, Spark, Kafka, etc.
• Experience with AWS cloud services: EC2, RDS, AWS-Sagemaker(Added advantage)
• Experience with object-oriented/object function scripting languages in any: Python, Java, C++, Scala, etc.

Responsibilities

• Selecting features, building and optimizing classifiers using machine learning techniques
• Data mining using state-of-the-art methods
• Enhancing data collection procedures to include information that is relevant for building analytic systems
• Processing, cleansing, and verifying the integrity of data used for analysis
• Creating automated anomaly detection systems and constant tracking of its performance
• Assemble large, complex data sets that meet functional / non-functional business requirements.
• Secure and manage when needed GPU cluster resources for events
• Write comprehensive internal feedback reports and find opportunities for improvements
• Manage GPU instances/machines to increase the performance and efficiency of the ML/DL model.

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