Description

Duties and Responsibilities: –

• Implement the application solving customer’s issue by applying AI/ML technologies
• Apply ML and DL models for various industry problems.
• Setting-up collaboration environment and framework for AI related research and development projects.
• Design and develop different architectural models for scalable data storage, processing, and large-scale analytics.
• Work with cross-functional teams to understand technical needs.
• Set-up big data environment that helps establish rapid POCs and prototype developments on both on-premises and cloud-based platforms.
• Monitor and optimize performance of the big data ecosystem.
• Ensure data accessibility to researchers via different programming languages.
• Keep up to date with state of the art in the industry.

Qualifications:

Minimum –

• Bachelor’s degree in Computer Science with substantial industry experience of 5 years or more of Data Engineering/ETL/Administration experience.
• Hands-on experience in Statistical data analysis and Machine learning
• Possess significant knowledge of Big Data technologies and tools.
• Good coding skills in at least one scripting language (Shell, Python, R, etc.)
• Experience with various Hadoop distribution like Hortonworks and Cloudera.
• Knowledge of cluster monitoring tools like Ambari, Ganglia, or Nagios.
• Delivered Big Data solutions in the cloud with AWS or Azure or Google Cloud.
• Experience in Java programming, Scala programming.
• Experience with RDBMS (MySQL, PostgreSQL, etc.)
• Experience with NoSQL database administration & development like MongoDB.
• Experience with Hadoop eco-system (MapReduce, Streaming, Pig, HIVE, Spark).
• Experience using DevOps toolbox such as Jenkins, Chef, Puppet.
• Proven ability to create and manage big data pipeline using Kafka, Flume & Spark
• Knowledge of BI tools such as Tableau, Pentaho, etc.
• Experience building large-scale distributed applications and services.
• Experience with agile development methodologies. –
• Knowledge of industry standards and trends.
• Good communication, logical thinking, and presentation skills.

Additional qualifications (preferred but not mandatory)–

• Master’s degree in Computer Science or equivalent with at least 5 years industry experience of data engineering/ETL/Administration.
• Experience applying Deep learning.
• Substantial industry experience developing prototypes and demonstrating PoCs