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AI AND MACHINE LEARNING ENGINEER

 Bangalore  2  TD-15

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Job Details

Company A leading IT company
Position AI AND MACHINE LEARNING ENGINEER
Required Experience 2-3 Years
CTC Best in the industry
Location Bangalore
Job Code TD-15
Posted On 10 Jan 2021
Application Deadline 10 May 2021

Skills


  • 2-3 years of experience with Big Data & Analytics solutions - Hadoop, MapReduce, Hive, Spark, Storm, Amazon Kinesis, AWS EMR, AWS Redshift, Lambda
  • Strong design and programming skills
  • Fluent with data structures and algorithms
  • Strong understanding of OOPs concepts
  • Hands-on experience with any of the following valuable, though not mandatory. You must be willing to learn the below technologies on a need basis.
  • Modern programming languages like Python and C++.
  • Spring Framework, Redis, RDBMS, NoSQL a plus.
  • Hadoop, Hive, Oozie, Map Reduce, Spark, Sqoop, Kafka, Flume, etc.
  • DevOps with experience building and deploying to CloudFoundry, Docker, Kubernetes
  • Infrastructure automation technologies like Docker, Vagrant, etc.
  • Build automation technologies like Gradle, Jenkins, etc.
  • Building APIs and services using REST, SOAP, etc.
  • Scripting languages like Perl, Shell, Groovy, etc.

Description


Job Duties & Required Skills

  • You will be responsible for delivering high-value next-generation products on aggressive deadlines and will be required to write high-quality, highly optimized/high-performance and maintainable code
  • Work on distributed/big-data system to build, release and maintain an Always On scalable data processing and reporting platform
  • Work on relational and NoSQL databases
  • Build scalable architectures for data storage, transformation and analysis
  • Experience with large scale Hadoop environments build and support including design, capacity planning, cluster set up, performance tuning and monitoring
  • Strong understanding of Hadoop eco system such as HDFS, MapReduce, Hadoop streaming, Sqoop, oozie and Hive
  • Managing available resources such as hardware, data, and personnel so that deadlines are met
    Analysing the ML algorithms that could be used to solve a given problem and ranking them by their success probability
  • Exploring and visualizing data to gain an understanding of it, then identifying differences in data distribution that could affect performance when deploying the model in the real world
  • Verifying data quality, and/or ensuring it via data cleaning
  • Supervising the data acquisition process if more data is needed
  • Finding available datasets online that could be used for training
  • Defining validation strategies
  • Defining the pre-processing or feature engineering to be done on a given dataset
  • Defining data augmentation pipelines
  • Training models and tuning their hyperparameters
  • Analysing the errors of the model and designing strategies to overcome them
  • Deploying models to production
  • Proficient in shell and Perl, python scripting
  • Experience in setup, configuration and management of security for Hadoop clusters.

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