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.
- 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.