Modernization Engineer

Kyndryl; Polska; Hybrid; PLN 180,000 - 260,000; Full time

Job summary

Kyndryl Inc. is seeking a Modernization Engineer to design, deploy, and operate generative AI solutions in a hybrid work model. You will work directly with customers to scope, prototype, and deploy agentic AI platforms, translating business needs into scalable technical implementations.

The role emphasizes accountability for outcomes, collaboration with researchers and product teams, and delivering measurable business value across on-site and remote settings.

Qualifications

• Bachelor’s degree in computer science, engineering, or an equivalent field.
• Minimum 3 years of hands-on experience building AI solutions with LangChain, Semantic Kernel, Google ADK, or Microsoft Agent Framework.
• Knowledge of LLMs, agent architectures, and telemetry/observability tools such as LangSmith, Langfuse, and LiteLLM.
• Expertise with Docker, Kubernetes, and at least one cloud platform—Azure, AWS, or GCP.
• Experience with microservices architectures and CI/CD.

Responsibilities

• Build autonomous agents using agentic platforms and LLMs, planning algorithms, and decision frameworks.
• Design agent architectures for autonomy, interaction, and task completion.
• Integrate agents with applications, APIs, workflows, databases, cloud platforms, and external services.
• Refine agent behavior through feedback loops and reinforcement learning.
• Monitor performance and implement safeguards for security, reliability, and responsible operation.
• Document agent logic, design decisions, dependencies, and deployment practices.

Skills

Python

TypeScript

React.js

LangChain

LangGraph

Agent Framework

Multi-agent systems

API development

CI/CD

GitHub Actions

Observability

Cloud platforms

Kubernetes

Docker

SQL

NoSQL

Pandas

Spark

Education

Bachelor’s degree in computer science, engineering, or an equivalent field

Tools

Docker

Kubernetes

Azure

AWS

GCP

LangSmith

Langfuse

LiteLLM

LangChain

Semantic Kernel

Job description

Modernization Engineers work directly with customers to design, deploy, and operate generative and agentic AI solutions. Combining deep engineering expertise with consultative engagement, they operate within Kyndryl’s engineering, consulting, and product triad and are accountable for business outcomes—not just code.

Role Description

At Kyndryl, we apply AI where it can deliver the greatest real-world impact. As a Modernization Engineer, you will work directly with customers to solve complex challenges and build solutions that can reshape industries. Supported by a global AI ecosystem, you will move quickly and play a central role in delivering meaningful outcomes through our integrated engineering, consulting, and product model.

This hybrid role goes beyond traditional consulting or software engineering. It is built for real-world AI deployment and emphasizes accountability for outcomes, not only code.

You will combine engineering depth with consultative skills to deliver tailored solutions that create measurable business value. Working closely with customer teams—often on-site—you will rapidly scope, prototype, and deploy generative AI, agentic AI, and multi-agent solutions. Lessons from each engagement will help strengthen Kyndryl’s platforms and accelerate future deployments.

This role is ideal for someone who enjoys solving complex problems, working directly with customers, and shaping the future of enterprise AI.

Key Responsibilities

• Build autonomous agents using Kyndryl’s Agentic Platform and LLMs, planning algorithms, and decision frameworks.
• Leverage and customerize pre-built autonomous agents available within Kyndryl’s Agentic Platform to maximize adoptiopn scale and consistency.
• Design agent architectures for autonomy, interaction, and task completion.
• Integrate agents with applications, APIs, workflows, databases, cloud platforms, and external services.
• Refine agent behavior through feedback loops, reinforcement learning, semantic knowledge layers, and user interactions.
• Monitor performance and implement safeguards for security, reliability, and responsible operation.
• Collaborate with researchers, engineers, product teams, and other cross‑functional partners.
• Document agent logic, design decisions, dependencies, and deployment practices.
• Build and maintain the Enterprise Agents and Tools Registry for metadata and lifecycle management.
• Implement the Agent Communication Gateway with security, rate limiting, observability, and cost controls.
• Apply agent and workflow orchestration patterns using tools such as Temporal and Airflow.
• Protect agents through authentication, authorization, and data security controls.
• Optimize cost, scalability, performance, and reliability across cloud and on‑premises environments.
• Use knowledge graphs to strengthen agent reasoning and data integration.
• Deploy and tailor agentic AI platforms, LLM agents, and orchestration frameworks.
• Resolve technical blockers in data ingestion, model deployment, and agent behavior.
• Design and refine prompts for clarity, compliance, and contextual accuracy.
• Translate business logic into agentic workflows and task trees aligned with real‑world expectations.
• Implement observability, performance metrics, and feedback loops to improve reliability, latency, trust, and outcomes.
• Build and iterate customer‑specific AI solutions using agentic AI frameworks.
• Own delivery from scoping through production, embedding with customer teams to deploy solutions that drive adoption and measurable business results.
• Customer Engagement & Solution Integrity: Translate customer challenges into high‑quality, fit‑for‑purpose solutions, prioritizing the right outcome over the fastest answer.
• Optimization: Diagnose and improve system performance, scalability, and reliability.
• Field Intelligence: Capture deployment lessons, document best practices, and share insights that improve Kyndryl’s platforms and frameworks.
• Continuous Learning: Stay current with emerging technologies and changing customer needs through ongoing development.

Required Technical and Professional Experience

• Bachelor’s degree in computer science, engineering, or an equivalent field.
• Full‑stack development experience with Python and front‑end technologies such as TypeScript and React.js, including building demos and systems from the ground up.
• Minimum 3 years of expertise with hands‑on experience building AI solutions with frameworks such as LangChain, Microsoft Semantic Kernel, Google ADK, or Microsoft Agent Framework.
• Knowledge of LLMs, agent architectures, and telemetry or observability tools such as LangSmith, Langfuse, and LiteLLM.
• Expertise with Docker, Kubernetes, and at least one cloud platform—Azure, AWS, or GCP—or on‑
• Experience with microservices architectures.
• Strong understanding of the software delivery lifecycle, Git and GitHub, and data engineering tools such as Pandas and Spark.
• Experience with cloud AI platforms and distributed computing architectures.
• Ability to translate business needs into technical solutions and explain their value to varied
• Hands‑on experience with SQL databases such as PostgreSQL, NoSQL databases such as MongoDB, and vector databases for storage, semantic search, and retrieval.
• Proficiency in CI/CD tools such as GitHub Actions, automated testing, and observability.
• Proficiency in API development, back‑end services, and AWS, Azure, or GCP.
• Practical experience deploying RAG architectures and integrating structured and unstructured knowledge sources.
• Open‑Source Ecosystems: Familiarity with community‑driven AI tools, libraries, and repositories,
• T‑Shaped Profile: Deep expertise in one or two areas, supported by broad knowledge of AI/ML, cloud, and consulting.
• Agentic AI Systems: Experience designing, building, or integrating multi‑agent systems and orchestration frameworks such as LangGraph, Semantic Kernel, Agent Framework, AutoGen, or CrewAI, including agent protocols and coordination mechanisms.
• Performance & Security: Knowledge of system optimization and security practices for scalable AI systems.

Travel

• Ability to travel globally a minimum of 25%.

Why Kyndryl?

Join a team that values speed, autonomy, and measurable impact. At Kyndryl, you will gain end‑to‑end visibility across the delivery lifecycle, with opportunities to lead engagements and shape strategy. Our triad model connects you with engineering, product, and consulting experts to drive continuous learning and innovation. With clear career paths from Junior to Principal, you can help shape our AI platforms, contribute directly to their development, and see your work deliver real‑world results. If you want your ideas to matter and your execution to make a difference, this is the place to do it.

Every role at Kyndryl offers opportunities to advance your career through hands‑on experience, continuous learning, and certifications across all four major platforms. Whether you want to expand your expertise or specialize in a specific sector, you can build your future here.

You bring the skills and experience required for the role, along with a growth mindset and a commitment to your personal and professional development. You put customer success first and work openly, collaboratively, and inclusively with others.

Kyndryl is committed to a diverse workplace and is proud to be an equal opportunity employer. We consider all qualified applicants without regard to race, color, religion, gender, gender identity or expression, sexual orientation, national origin, genetics, disability, age, or veteran status. We also comply with all fair employment practices related to citizenship and immigration status.