5+ years of strong experience in building maintaining machine deep learning models
Experience in performing detailed exploratory data analysis (EDA) having sound statistical knowledge
Understanding of concepts related to algorithm and deep learning architectures
Develop prototypes, proof of concepts, algorithms, predictive models, and custom analysis
Analysis of structured and unstructured data from problem statement to model deployment business impact using ML/DL/AI
Should be hands on AWS cloud services that includes VPC, EC2, S3, RDS, Redshift, Data Pipeline, EMR, DynamoDB, DevOps, Lambda, Kinesis, DMS, SNS, SQS, Sagemaker, Apache Airflow, and EKS.
Proficient in big data ingestion and streaming tools like Amazon Kinesis, Beam or Kafka. [SB4]
Experience in deploying models via APIs and on the edge devices in a secure manner.
Good Knowledge/Understanding of NoSQL data bases and hands on work experience in writing applications on NoSQL databases like Cassandra and MongoDB
Good knowledge on various scripting languages like Linux/Unix shell scripting and Python
Good knowledge of Datawarehousing concepts and ETL processes using Redshift SQL Server
Experienced in using IDEs and Tools like GitHub, AWS CodeCommit, Jupyter Notebooks/Lab and Anaconda.
Strong team player, ability to work independently and in a team as well, ability to adapt to a rapidly changing environment, commitment towards learning, documentation skills
Sound practical knowledge of statistical packages like - R Python
Creating data visualisations to effectively convey findings using Tableau.
Technical Skills:
Cloud Platform: AWS
Big Data: Python, Pytorch, Torchserve, Airflow, TensorFlow
Databases: Redshift and SQL Server.
Visualization: Tableau or Quicksight
Languages: SQL, TensorFlow, Pytorch and Python
Tools/IDE: Docker, Kubernetes, Airflow, Jupyter Notebooks/Labs and Anaconda