Modernization Engineer

Overview

Modernization Engineer role at Kyndryl building, deploying and optimizing autonomous and multi-agent AI solutions for customers, working directly with customer teams to scope, prototype and deliver production AI implementations.

What you'll do

• Build, customize and deploy autonomous agents using Kyndryl's Agentic Platform, LLMs, prompt design, knowledge graphs and task trees to ensure high contextual accuracy.
• Connect agents across APIs, databases, hybrid-cloud platforms and external services using orchestration tools such as Temporal and Airflow, alongside an Enterprise Agents and Tools Registry.
• Implement the Agent Communication Gateway with robust authentication, authorization, rate limiting, cost controls and safety safeguards.
• Refine agent performance, latency and reasoning using feedback loops, reinforcement learning, semantic knowledge layers and end-to-end telemetry metrics.
• Lead solution delivery from initial scoping to production, translating complex customer needs into fit-for-purpose, high-impact AI implementations.
• Work with researchers, engineering and product teams to share operational lessons, establish best practices and document agent logic and deployment frameworks.

What you'll need

• 3+ years of experience building AI solutions, multi-agent systems and orchestration frameworks using LangChain, LangGraph, AutoGen, CrewAI or Semantic Kernel.
• Hands-on full-stack development experience using Python, React.js and TypeScript, along with microservices architecture, back-end services and RESTful API development.
• Proficiency in SQL (PostgreSQL), NoSQL (MongoDB) and vector databases, with practical experience deploying RAG architectures for structured and unstructured data.
• Strong background in Docker, Kubernetes, CI/CD pipelines (GitHub Actions), Git and deployment across major cloud platforms (AWS, Azure or GCP).
• Ability to translate complex business needs into technical solutions and effectively communicate their value to executive-level stakeholders.

Nice to have

• Bachelor's degree in CS/Engineering (or equivalent).
• Experience implementing AI telemetry and monitoring tools such as LangSmith, Langfuse and LiteLLM to track agent behavior, latency and performance.
• Familiarity with data engineering libraries and distributed frameworks, including Pandas, Spark and cloud-native AI platforms.
• Deep understanding of system performance tuning, scalability, security controls and authentication protocols for enterprise AI operations.
• Familiarity with community-driven AI libraries, models and repositories, including Hugging Face, fine-tuning workflows and open-source models.
• Broad cross-functional exposure across AI/ML and cloud consulting, combined with deep technical specialization in core software delivery practices.

Details

• Hybrid role, working closely with customer teams, often on-site.