Responsibilities
Partner with Analytics and AIML teams to develop and analyze features at scale. Provide SME level interface for team members to optimize their workflows, streamline operationalization and reduce time-to-market
- Develop distributed applications on-prem as well as on Cloud that scale to serve ML models, analytics, rules, web-applications, and Visualizations for end-users
- Production deployment and Model monitoring to ensure stable performance and adherence to standards
- Develop libraries to ease development, monitoring and control of data and models
- Identify potential improvements to the current design/processes.
Primary skills
- Experienced professional with 8 – 11 years of experience working towards design, architecture, development, and operationalization of Data Engg & AI/ML models across Big Data Ecosystem (PySpark, Hadoop, Snowflake, Python)
- Experience in architecture, design, and implementation of data intensive applications for practical use-cases
- Experience and understanding Java/C++
- Experience working in Linux environment involving writing shell scripts, troubleshooting business user issues, Familiar with NFS shares, resolving permission issues and space issues.
Good to have skills
- Experience in the Financial Industry.
- Knowledge of Data Warehouse (Teradata, Oracle, SQL server, etc)
- Knowledge of cloud platforms
- Experience working in and Kanban Team
- Knowledge of CI/CD Pipelines GitHub Actions
Qualifications
- Bachelor's or Master's degree in Computer Science or related field, or equivalent job experience
- 8+ years of experience in software development
- 3+ years of experience architecting distributed systems
- 2+ years of experience building ML applications
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