Agentic AI Engineer – Mainframe Modernization
Kyndryl (company site, opens in a new tab)5–8 yrsBengaluruOn-siteDirect career pageEst. 22–40 LPA
Posted 14h ago
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Job description
Structured overview of role & requirements
About This Role
- Deploy and scale agentic AI, RAG and code-aware workflows for mainframe discovery, reverse engineering and modernization across COBOL, PL/I, JCL, CICS, IMS and DB2.
- Turn AI analysis into modernization deliverables—business rules, dependency maps and architecture artifacts—and support refactoring and cloud migration.
- Integrate AI into enterprise tools and CI/CD pipelines; establish evaluation, guardrails and human validation to improve output quality, security and reliability.
Minimum Requirements
- Work Experience Required: 5–8+ years in software engineering, including 2+ years building and deploying GenAI, LLM or Agentic AI solutions in enterprise production environments.
- Hands-on mainframe modernization experience, including legacy application discovery, reverse engineering, code understanding and dependency analysis; relevant technologies include COBOL, PL/I, JCL, CICS, IMS and DB2.
- Strong Agentic AI engineering skills, including orchestration, multi-agent systems, prompt/context engineering, RAG, vector databases and knowledge systems.
- Python proficiency and experience integrating enterprise tools and APIs; hands-on Git/GitHub, CI/CD, SDLC and secure development practices.
Ideal Candidate Profile
- Combines production-grade AI engineering with practical mainframe modernization experience, and can translate analysis into implementation-ready transformation outputs.
- Has experience embedding AI into enterprise engineering workflows and validating generated code, tests or migration artifacts using governance and human-in-the-loop controls.
- KAF experience and a track record of building reusable AI modernization accelerators are preferred.