Agentic AI Engineer – Mainframe Modernization
Kyndryl (company site, opens in a new tab)5–8 yrsBengaluruOn-siteDirect career pageEst. 22–40 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 for mainframe discovery, reverse engineering, documentation, and transformation.
- Produce modernization outputs such as extracted business rules, dependency maps, and architecture artifacts to support refactoring and cloud migration.
- Integrate AI into enterprise tools, repositories, and CI/CD pipelines; establish evaluation, security guardrails, and human validation to improve output quality 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.
- Strong hands-on expertise in agentic AI frameworks and orchestration, multi-agent systems, prompt/context engineering, RAG, vector databases, semantic search, and knowledge systems; proficiency in Python.
- Mainframe modernization experience covering legacy discovery, reverse engineering, code understanding, and dependency analysis across technologies including COBOL, PL/I, JCL, CICS, IMS, and DB2.
- Experience with Git/GitHub, CI/CD, SDLC, DevSecOps, secure development, code analysis and testing, plus AI evaluation, validation, guardrails, and HITL workflows.
Ideal Candidate Profile
- Has delivered production-grade AI workflows that connect code-aware analysis to usable engineering artifacts and modernization activities.
- Combines enterprise AI engineering depth with practical mainframe knowledge, including legacy-to-cloud transformation and code refactoring contexts.
- Can work across mainframe, architecture, cloud, and transformation teams, while building reusable AI accelerators and guiding teams in AI-assisted delivery.