Agentic AI Engineer – Mainframe Modernization
Kyndryl (company site, opens in a new tab)5–8 yrsBengaluruOn-siteDirect career pageEst. 18–35 LPA
Posted 15h ago
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Job description
Structured overview of role & requirements
About This Role
- Deploy and scale Agentic AI, code-aware agents, and RAG workflows to automate discovery, reverse engineering, and modernization of COBOL, PL/I, JCL, CICS, IMS, and DB2 applications.
- Turn AI analysis into usable modernization outputs—business rules, dependency maps, architecture artifacts, and support for refactoring and cloud migration; integrate workflows into enterprise tools and CI/CD pipelines.
- Own AI output quality and governance through evaluation frameworks, guardrails, security controls, and human review; develop reusable accelerators and support modernization teams.
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 hands-on expertise in Agentic AI frameworks and orchestration, multi-agent systems, prompt/context engineering, RAG, vector databases, semantic search, embeddings, and knowledge systems; proficiency in Python for automation, APIs, and integrations.
- Hands-on mainframe modernization experience, including application discovery, reverse engineering, code understanding, and dependency analysis across COBOL, PL/I, JCL, CICS, IMS, and DB2.
- Experience integrating AI solutions with enterprise tools and development pipelines; working knowledge of Git/GitHub, CI/CD, SDLC, DevSecOps, software quality, AI evaluation, guardrails, and validation.
Ideal Candidate Profile
- A senior, production-focused AI engineer who can move from agent and knowledge-system design through enterprise integration, validation, and scalable delivery.
- Combines practical mainframe analysis and legacy-to-cloud modernization experience with code transformation, testing, and implementation-ready documentation outputs.
- KAF experience and a record of creating reusable modernization accelerators, validating AI-generated code or migration assets, and enabling teams would be advantageous.