Senior Software Developer / Technical Lead with an AI Focus

Kyndryl•Dallas, TX

5d•$143,640 - $273,000

About The Position

Kyndryl is seeking a hands-on Senior Software Developer / Technical Lead with an AI focus for its AI Led Application Modernization team. The team builds new applications and modernizes existing ones, including a wide range of systems from mainframe and Fortran applications to J2EE and ASP web applications. It uses and continues to improve Kyndryl’s Agentic Framework and operates with a startup mentality. The role combines software development with technical leadership. The successful candidate will lead developers through new application builds and modernization programs while contributing directly to design, coding, review, testing, and troubleshooting. They will guide architecture and engineering practices, use AI-assisted development tools, and create fit-for-purpose agents and agentic workflows when appropriate. The position emphasizes responsible AI adoption and ensuring software remains high-quality, secure, maintainable, and aligned with business needs. The role also involves analyzing legacy applications, identifying business logic and constraints, defining target architectures, and planning incremental transformations. The technical lead will work with architects, product owners, business stakeholders, client technical teams, and delivery leaders to translate business needs into executable plans, mentor developers, manage technical risks, and create reusable engineering patterns and accelerators. The U.S. compensation range is $143,640–$273,000 for a full-time schedule, with different ranges for certain locations. The position is eligible for Kyndryl’s discretionary annual bonus program, subject to applicable plan terms. A comprehensive benefits package may include medical and dental coverage, disability, retirement benefits, paid leave, and paid time off. Applications are accepted on a rolling basis.

Requirements

At least 7 years of hands-on software development experience delivering production-quality applications.
Experience leading developers, including mentoring, code review, technical planning, delivery guidance, and issue resolution.
Experience setting technical direction for software delivery projects, including architecture decisions, engineering standards, delivery patterns, and technical risk management.
Experience with at least one modern programming language, such as Java, Python, JavaScript/TypeScript, C#, or Go, or comparable technologies.
Experience with new application development, application modernization, or both.
Practical experience using AI-assisted development tools for coding, refactoring, testing, documentation, debugging, code comprehension, or architecture exploration.
Ability to review and validate AI-generated code for correctness, security, maintainability, performance, testability, and alignment with architecture and 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 architecture and engineering fundamentals, including APIs, data models, integration patterns, automated testing, version control, CI/CD, secure coding, observability, and operational support.
Ability to analyze existing codebases, identify modernization opportunities, define target-state architecture, and create incremental transformation plans.
Ability to communicate technical direction to developers, architects, stakeholders, and client teams.
Growth mindset and enthusiasm for the effect of AI on software development.

Nice To Haves

Experience modernizing legacy applications, including mainframe, Fortran, J2EE, ASP, monolithic, or other enterprise application estates.
Experience architecting cloud-native applications, APIs, microservices, event-driven systems, or modern front-end experiences.
Hands-on experience with LangGraph, A2A, MCP, OpenAI or Anthropic APIs, Semantic Kernel, CrewAI, or comparable agent and orchestration frameworks.
Experience designing agents that interact with enterprise tools, repositories, documentation, tickets, CI/CD pipelines, runtime telemetry, or application data.
Experience defining architecture guardrails, reference implementations, coding standards, reusable components, or engineering playbooks.
Familiarity with DevSecOps practices, automated quality gates, observability, containerization, infrastructure as code, and secure software delivery pipelines.
Experience with retrieval-augmented generation, vector databases, tool calling, evaluation harnesses, prompt engineering, LLM application testing, or agent observability.
Experience in Agile delivery environments with product owners, architects, business stakeholders, client executives, and distributed engineering teams.
Ability to mentor and influence developers in practical, responsible, and effective AI-assisted software development.

Responsibilities

Lead developers through new application builds and modernization programs while contributing to design, coding, review, testing, and troubleshooting.
Set technical direction for projects, including architecture, integration patterns, modernization strategy, engineering standards, delivery approach, and technical risk management.
Use AI-assisted development tools for code comprehension, generation, refactoring, testing, documentation, and migration planning, validating outputs with engineering judgment.
Architect and build fit-for-purpose agents and agentic workflows using frameworks such as LangGraph, A2A, MCP-enabled tools, or comparable technologies.
Analyze legacy code, identify business logic and constraints, define target architecture, and create incremental modernization roadmaps.
Make architecture decisions across APIs, data models, cloud services, security, observability, integration, DevSecOps, and user experience.
Mentor developers in engineering practices, AI-assisted delivery, secure coding, automated testing, maintainable design, and code review.
Collaborate with architects, product owners, business stakeholders, client technical teams, and delivery leaders to turn business needs into executable technical plans.
Identify and manage risks in AI-generated software, including correctness, security, testing, licensing, maintainability, and architecture drift.
Create reusable patterns, accelerators, prompts, agents, reference architectures, and engineering practices for modernization delivery.