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Mid-level backend title, metro location, and common skillset increase competition despite mainframe niche.
Mainframe and COBOL focus ties strongly to legacy financial systems, reducing cross-industry transferability.
Explicit 3–5 years and mandatory Python, shell, and mainframe (COBOL/JCL/DB2) skills make screening stringent.
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Develop automation scripts and manual process automations using Python and Shell scripting.
Build and maintain GenAI and Agentic AI solutions at enterprise level.
Develop, maintain, and test data masking algorithms for mainframe applications, including resolving off-hour production issues.
3 to 5 years of experience as a Python Developer focused on data masking.
Strong working experience in Python and Shell scripting.
Experience with mainframe development and production support including JCL, COBOL, VSAM, and DB2.
Exposure to GenAI is mandatory; Agentic AI exposure is good to have.
Candidates with combined expertise in Python automation and mainframe technologies who can operate across SDLC phases.
Experience in enterprise-level AI solutions, particularly GenAI and preferably Agentic AI.
Capable of handling production support responsibilities including off-hour issue resolutions.