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Agentic AI Engineer – Mainframe Modernization
Kyndryl (company site, opens in a new tab)5–8 yrsBengaluruOn-siteDirect career pageEst. 20–35 LPA
Posted 15h ago
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Job description
Structured overview of role & requirements
About This Role
- Deploy and scale agentic AI workflows, RAG pipelines and code-aware agents for legacy application discovery and reverse engineering across mainframe systems.
- Turn AI analysis into modernization deliverables—business rules, dependency maps and architecture artifacts—and support refactoring and cloud migration.
- Integrate AI into enterprise repositories and CI/CD pipelines; establish evaluation, security and human-validation guardrails, and build reusable accelerators.
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.
- Strong expertise in agentic AI frameworks, multi-agent systems, workflow orchestration, prompt and context engineering, RAG, vector databases, semantic search and related knowledge systems; strong Python skills.
- Hands-on mainframe modernization experience, including discovery, reverse engineering and dependency analysis across technologies such as COBOL, PL/I, JCL, CICS, IMS and DB2.
- Experience integrating AI with enterprise tools and CI/CD; knowledge of Git, SDLC, DevSecOps, secure development, software quality, AI evaluation, guardrails and validation.
Ideal Candidate Profile
- Has worked across AI engineering and mainframe modernization, translating legacy-code analysis into implementation-ready outputs for refactoring or migration.
- Can embed AI automation into existing engineering workflows and improve its accuracy, reliability and scalability through evaluation and human review.
- Experience with Kyndryl Agentic Framework (KAF), reusable modernization accelerators, code-aware agents or AI solutions in regulated or security-sensitive environments is a shortlist advantage.