At Fujitsu, our purpose is to make the world more sustainable by building trust in society through innovation. Founded in Japan in 1935, Fujitsu has been a pioneer in technology and innovation for decades. Today, as a world-leading digital transformation partner, we are committed to transforming business and society in the digital age.
With approximately 130,000 employees across over 50 countries, Fujitsu offers a broad range of products, services, and solutions. We collaborate with our customers to co-create solutions that drive enterprise-wide digitalization while actively working to address social issues and contribute to the United Nations Sustainable Development Goals (SDGs).
About the role :
The Mainframe Modernization Expert & AI Software Developer delivers application and persistence (database, files, etc.) modernization projects using AWS Transform for mainframe refactor, formerly AWS Blu Age. The role bridges legacy-platform understanding, cloud-native Java engineering, automated functional-equivalence validation, and agentic software-development practices.
The engineer contributes across the MM2Agentic lifecycle, from codebase assessment and calibration through proof of concept, mass refactoring, verification, validation, and preparation for agentification.
This is a delivery position, with an advisory role dimension. The successful candidate is expected to work directly with source code, generated applications, transformation rules, test evidence, defects, performance issues, security controls, and customer technical stakeholders.
Key responsibilities :
1. Application assessment and modernization planning:
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Analyze legacy application inventories, source repositories, dependencies, entry points, interfaces, data flows, and technical complexity.
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Read and understand COBOL, PL/I, RPG, JCL, and associated mainframe artifacts sufficiently to support transformation and remediation.
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Contribute to compatibility analysis, scope extraction, component classification, migration-wave planning, technical-risk identification, and pilot selection.
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Help define relevant technical acceptance criteria, test strategy, delivery assumptions, and modernization backlog.
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Support codebase assessments that produce a modernization scope, ROM budget, macro schedule, RACI, risks, acceptance criteria, and recommended Pilot, POC, or POV.
2. AWS Transform configuration and refactoring
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Deliver automated refactoring activities using AWS Transform for mainframe refactor.
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Support configuration and calibration of the transformation environment and transformation rules.
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Analyze generated code, transformation exceptions, unsupported patterns, and discrepancies between legacy and target behavior.
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Diagnose issues originate from source-code interpretation, transformation rules, generated Java, runtime behavior, data conversion, or target architecture.
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With support of AWS Product help desk, implement repeatable fixes and feed proven remediation patterns back into the transformation and regeneration process.
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Support transformation of applicable IBM Z, IBM iSeries / AS400, Fujitsu GS21, or Micro Focus application estates.
3. Modern Java and cloud-native engineering
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Review, troubleshoot, improve, and extend the Java applications and database attached generated through the refactoring process.
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Develop Java services using Spring Boot, Quarkus, or the target framework defined by the project architecture.
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Implement and maintain REST APIs, integration services, data-access components, batch services, and message-based integrations.
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Contribute to front-end remediation or integration where modernized applications use Angular or React.
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Address code-quality, maintainability, security, and performance issues in the modernized application.
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Support the decomposition of selected functionality into maintainable services where this is included in the approved target architecture.
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Ensure that generated and remediated code remains traceable to the transformed legacy behavior and agreed acceptance evidence.
4. Functional-equivalence engineering
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Design and implement automated comparisons between legacy-system outputs and modernized-application outputs.
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Capture representative test cases and replay them against the modernized environment.
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Compare screens, files, messages, database results, batch outputs, API responses, and other externally observable behavior, according to project scope.
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In collaborating with Customer’s SMEs, investigate discrepancies, perform root-cause analysis, implement remediation, and rerun tests until the agreed acceptance conditions are met.
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Build regression suites that can be executed continuously through the project’s CI/CD and continuous-testing pipelines.
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Produce traceable acceptance evidence for functional, integration, performance, and code-quality criteria.
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Work with customer SMEs to identify untested entry points, missing test data, and business scenarios requiring validation.
5. AI-assisted software development
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Use AWS Kiro, Claude Code, MCP-based tools, and other approved AI engineering capabilities to accelerate reverse engineering, code remediation, test generation, discrepancy analysis, and technical documentation.
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Design controlled agentic workflows for repeatable engineering tasks rather than relying on ungoverned, one-time prompts.
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Build supporting tools in Python or Java for source analysis, test generation, code-quality remediation, result comparison, and engineering automation.
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Connect AI agents to approved tools and project evidence through Model Context Protocol where appropriate.
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Introduce evaluation loops that compare expected and actual results before accepting AI-generated changes.
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Maintain human review, source control, test evidence, and security controls for AI-generated code.
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Contribute reusable accelerators and lessons learned to the MM2Agentic modernization factory.
6. DevOps and delivery automation
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Support build, deployment, and automated-testing pipelines for transformed applications.
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Work with Git, CI/CD, Docker, AWS services, test environments, and project observability.
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Integrate code-quality and security controls into the development workflow.
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Investigate performance bottlenecks in generated or remediated applications.
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Conduct or support threat modelling, application-security reviews, IAM design, encryption, logging, and monitoring.
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Ensure that customer source code and test data are processed in accordance with the project’s security architecture.
7. Technical leadership and customer collaboration
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Lead or coordinate a delivery workstream within a larger modernization factory.
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Define engineering standards, review code, distribute technical work, and monitor defect resolution.
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Explain modernization findings and technical decisions to customer architects, developers, testers, security teams, and application owners.
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Escalate transformation blockers with clear evidence, reproducible examples, impact analysis, and recommended resolution.
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Coach engineers on AWS Transform, generated-code remediation, functional-equivalence testing, and AI-assisted development.
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Create reusable technical guidance, workshops, and examples for the wider delivery team.
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Collaborate with mainframe specialists, cloud architects, data-migration engineers, test leads, DevOps engineers, and AWS specialists.
AI engineering requirements :
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Hands-on use of AI coding agents such as AWS Kiro or Claude Code.
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Experience designing agentic engineering workflows.
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Practical knowledge of Model Context Protocol.
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Ability to use LLMs with structured evaluation and validation, rather than accepting generated code without evidence.
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Experience developing productivity or remediation tools using Python.
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Understanding of RAG, embeddings, AI-agent frameworks, and tool-based orchestration.
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Ability to apply enterprise security and governance controls to AI-assisted development.
At Fujitsu, we are committed to an inclusive recruitment process that values the diverse backgrounds and experiences of all applicants. We believe that hiring people from a wide variety of backgrounds makes us stronger, not because it's the right thing to do, but because it allows us to draw on a wider range of perspectives and life experiences.