Agentic AI Engineer – Mainframe Modernization
Kyndryl (company site, opens in a new tab)5+ yrsBengaluruOn-siteDirect career pageEst. 20–35 LPA
Posted 16h ago
Reach people at Kyndryl
People at this company you can reach
Log in to see who works here
Create a free account or log in to see the people at Kyndryl you could write to:
- Recruiters & HR
- People in this function
- Work addresses
Your match
Against your primary resume
Log in to see your match score
Create a free account or log in to unlock your CV match score across:
- Role fit
- Experience
- Industry relevance
Protocol Intelligence
How competitive this role is
Competition
Background fit
Strictness
Log in to see why each signal reads the way it does.
Job description
Structured overview of role & requirements
About This Role
- Deploy and scale agentic AI workflows, RAG pipelines, and code-aware agents to accelerate discovery and reverse engineering of mainframe applications.
- Turn AI analysis into modernization deliverables—business rules, dependency maps, architecture artifacts, and support for refactoring and cloud migration; integrate workflows with enterprise tools and CI/CD pipelines.
- Own AI output quality and governance through evaluation frameworks, guardrails, security controls, and human validation; develop reusable accelerators and guide 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 Python and hands-on expertise in agent frameworks, workflow orchestration, prompt/context engineering, RAG, vector databases, semantic search, and knowledge systems.
- Hands-on mainframe modernization experience, including discovery, reverse engineering, code understanding, and dependency analysis across technologies such as COBOL, PL/I, JCL, CICS, IMS, and DB2.
- Experience integrating AI with enterprise tools, repositories, and CI/CD; background in secure development, software quality, AI evaluation, guardrails, and human-in-the-loop validation.
Ideal Candidate Profile
- Has worked across both enterprise AI engineering and mainframe modernization, and can translate legacy-code analysis into implementation-ready engineering outputs.
- Has production experience connecting AI workflows to existing engineering pipelines and validating generated code, tests, and documentation for accuracy and security.
- Direct KAF experience and a record of creating reusable AI modernization assets are preferred; experience in regulated or security-sensitive environments is also a plus.