Software Developer with an AI Focus

Kyndryl•No City (KUS51413) Florida Default WKL, FL

1d•$92,040 - $174,840•Remote

About The Position

Kyndryl is seeking a hands-on Software Developer with an AI focus to help us build new applications and modernize existing ones for our clients. You will work as part of an AI Led Application Modernization team that believes the way software is designed, built, tested, and maintained is changing quickly—and that this change creates a tremendous opportunity for developers who are curious, practical, and ready to learn. This role is deeply hands-on. You will write code, review AI-generated output, build automated tests, improve delivery patterns, and help shape how our team uses AI responsibly to deliver better software faster.

Requirements

3 years of software development experience delivering production-quality applications.
Experience with at least one modern programming language such as Java, Python, JavaScript/TypeScript, C#, Go, or comparable technologies.
Experience with either new application development, application modernization, or both.
Practical experience using AI-assisted development tools to support coding, refactoring, testing, documentation, debugging, or code comprehension.
Experience reviewing and validating AI-generated code for correctness, security, maintainability, and alignment with requirements.
Familiarity with agentic development concepts and experience building or prototyping fit-for-purpose agents using LangGraph, A2A, MCP-enabled tools, or similar frameworks.
Strong understanding of software engineering fundamentals, including APIs, data models, automated testing, version control, CI/CD, and secure coding practices.
Ability to analyze existing codebases, identify modernization opportunities, and contribute to incremental transformation plans.
Growth mindset and enthusiasm for how AI will reshape the software development profession.
Hands-on experience with at least one of LangGraph, A2A, MCP, OpenAI or Anthropic APIs, Semantic Kernel, CrewAI, or comparable agent and orchestration frameworks… we understand that these are not yet part of a developers life at every company, but if you haven’t worked with these at work we expect you to be playing with them in your spare time.

Nice To Haves

Experience with QA Methods and Best Practices.
Experience applying AI to QA.
Experience modernizing legacy applications, including mainframe, Fortran, J2EE, ASP, monolithic, or other enterprise application estates.
Experience building cloud-native applications, APIs, microservices, event-driven systems, or modern front-end experiences.
Experience creating agents that interact with enterprise tools, repositories, documentation, tickets, CI/CD pipelines, or application runtime data.
Familiarity with DevSecOps practices, automated quality gates, observability, containerization, and secure software delivery pipelines.
Experience with retrieval-augmented generation, vector databases, tool calling, evaluation harnesses, prompt engineering, or LLM application testing.
Experience working in Agile delivery environments with product owners, architects, business stakeholders, and distributed engineering teams.
Ability to mentor other developers in practical, responsible, and effective use of AI-assisted software development practices.

Responsibilities

Use AI-assisted development tools such as Claude Code, Codex, GitHub Copilot, and similar technologies to accelerate delivery while maintaining engineering discipline, code quality, and security.
Design, build, test, and refactor software across new development and modernization efforts, including applications that may span legacy platforms, enterprise systems, APIs, cloud-native services, and modern user experiences.
Create fit-for-purpose AI agents and agentic workflows using frameworks such as LangGraph, A2A, MCP-enabled tools, or similar approaches when they are the right solution for a client or delivery challenge.
Design, develop, test, and maintain software for new application builds and modernization programs.
Use AI-assisted development tools to generate, explain, refactor, test, and document code while applying strong engineering judgment to validate the results.
Build fit-for-purpose agents and agentic workflows using frameworks such as LangGraph, A2A, MCP-enabled tools, or comparable technologies.
Modernize existing applications by analyzing legacy code, extracting business logic, improving architecture, and incrementally moving systems toward more maintainable patterns.
Collaborate with architects, product owners, business stakeholders, and other developers to translate requirements into working software.
Apply sound software engineering practices, including clean code, automated testing, secure coding, code reviews, CI/CD, observability, and maintainable documentation.
Evaluate where AI can improve the software development lifecycle, including requirements analysis, code comprehension, test generation, documentation, migration planning, and developer productivity.
Identify risks in AI-generated software, including brittle code, hidden assumptions, weak tests, security issues, licensing concerns, and maintainability gaps.
Contribute to reusable patterns, accelerators, prompts, agents, and engineering practices that help the broader team deliver modernization work more effectively.
Stay current with emerging AI development tools and agentic frameworks, bringing a practical point of view on what is ready for enterprise use.