Senior Modernization Architect

Kyndryl•West Monroe, LA

2d•$179,760 - $341,520

About The Position

At Kyndryl, we design, build, manage and modernize the mission-critical technology systems that the world depends on every day. We are seeking a hands-on Modernization Architect who combines deep expertise in agentic AI with a proven ability to transform legacy systems into intelligent, composable platforms. In this client-facing role, you will lead engagements from discovery through MVP — assessing existing architectures, designing modernization roadmaps, and building custom AI agents and automated workflows that unlock lasting business value. You will be equally comfortable whiteboarding a migration strategy with an enterprise CTO and writing the orchestration code that brings that strategy to life.

Requirements

10+ years of experience in software engineering, solution architecture, or technical consulting
2+ years of hands-on experience designing and building agentic AI or LLM-powered systems in production environments
Demonstrated expertise in modernizing legacy architectures — mainframes, monoliths, or on-premises systems — to Modern platforms, including cloud-native
Proficiency with at least one agentic orchestration framework: LangGraph, AutoGen, CrewAI, Semantic Kernel, or equivalent
Strong programming skills in Java and Python; experience with TypeScript/Node.js
Hands-on experience with cloud platforms (AWS, Azure, or GCP) and container technologies (Docker, Kubernetes)
Experience designing RESTful and event-driven integrations (Kafka, RabbitMQ, Azure Service Bus, or similar)
Excellent communication skills — able to translate technical complexity for executive audiences without losing precision

Nice To Haves

Experience with AI-assisted code transformation tools (e.g., GitHub Copilot, Amazon Q Developer, custom migration agents)
Background with legacy languages or platforms: COBOL, PL/SQL, VB6, .NET Framework, WebSphere, or IBM MQ
Familiarity with database migration strategies: schema conversion, CDC pipelines, and polyglot persistence
Certifications: AWS Solutions Architect Professional, Azure Solutions Architect Expert, Google PCA, or TOGAF

Responsibilities

Assess legacy codebases, monolithic architectures, and on-premises infrastructure to define targeted modernization strategies (re-platform, re-factor, re-architect, replace)
Design cloud-native target architectures leveraging microservices, event-driven patterns, API-first design, and containerization (Docker/Kubernetes)
Lead technical discovery workshops to inventory systems, dependencies, data flows, and integration points
Produce modernization roadmaps with phased milestones, risk registers, and TCO/ROI analysis
Define migration patterns for databases, middleware, and mainframe workloads to modern cloud platforms
Design and build custom AI agents and multi-agent workflows tailored to client-specific business processes and modernization objectives
Implement agentic orchestration using frameworks such as LangGraph, AutoGen, CrewAI, Semantic Kernel, or using the Kyndryl Agentic Platform
Create intelligent automation pipelines that replace or augment legacy batch jobs, manual handoffs, and rule-based workflows
Build/Enhance AI-assisted code migration agents capable of analyzing, transforming, and validating legacy code (COBOL, VB6, PL/SQL, .Net, etc.)
Lead client-facing discovery sessions, architecture reviews, and modernization readiness assessments
Translate legacy estate complexity into clear business risk narratives and modernization business cases
Serve as a trusted technical advisor to senior stakeholders, guiding both strategy and sequencing
Facilitate workshops to align business owners, engineers, and operations teams around target state architectures
Produce actionable deliverables: strategy decks, architecture blueprints, migration playbooks, and agent workflow specifications
Design and deliver live demonstrations of agentic modernization capabilities, including code transformation, intelligent workflow automation, and AI-assisted operations
Build reusable demo environments and MVPs that showcase modernization outcomes at different stages of a client journey
Present modernization strategies and AI workflow designs to both technical teams and executive audiences
Scope, design, and deliver production-ready AI agents based on client-supplied requirements and use cases
Lead joint development sessions ("AI pairing") with client engineering teams to accelerate capability transfer
Define agent contracts: input/output schemas, tool definitions, memory strategies, fallback behaviors, and escalation paths
Establish quality gates: unit tests, integration tests, and evaluation harnesses for agent reliability and output quality