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Understanding and exposure to the Software Development Life Cycle (SDLC), Agile methodologies and extreme programming techniques (Unit testing and Build Automation).Experience in training Deep Learning models on GPUs using frameworks Caffe and TensorFlow and Pytorch.Deep understanding of Statistical modeling, Multivariate Analysis, Model Testing, Engineering Analysis, Model Comparison and Validation.Built, trained, deployed Machine Learning models in a production level environment.Proficient in managing the entire machine learning project life cycle and actively involved in all phases of project life cycle including data acquisition, features engineering, model training and testing.Proficient in Data Mining Methods, Factor Analysis, Hypothetical testing, Distributions, Time-Series Analysis, and other Advanced Statistical Techniques.Experience in automation of manual processes/tasks using Python /AWS.Extensive experience in using different modeling techniques like Decision Trees, Regression Models, Neural Networks, SVM, Clustering, Dimensionality Reduction using Principal Component Analysis and Deep Learning.Experience with NLP frameworks like Spacy, NLTK, Scikit-Learn, HuggingFace, etc.,Experience in working with data in different formats CSV, Parquet, JSON, ect.,Thorough knowledge on NoSQL databases like AWS DynamoDB, AWS MongoDB and Redis.Experience in working with AWS Cloud services EC2, RDS, SNS, SQS, Lambda, S3, Glue, Redshift, StepFunctions, etc.,Experience with working with Azure Cloud, Azure Synapse Analytics, Azure Cosmos NoSQL DB, Azure Devops.Good knowledge of building datalakes, deltalakes with UPSERT operations using Apache Hudi for streaming data.Experience in building/deploying Cloud Services using Serverless framework.Experience in using version control systems like SVN, Git in software development environments.Good understanding of theoretical concepts related to computer vision and real time experience in using OpenCV.3.x in production environment.