AI Platform Engineer

Kyndryl; Hoofddorp; On-site; EUR 90,000 - 120,000; Full time

Job summary

Kyndryl is strengthening its AI and ML capabilities on Microsoft Azure to deliver intelligent, secure, and scalable digital solutions. This role focuses on designing, building, and operationalizing a central AI Platform, including AI SDK development, data masking, PII detection, and document intelligence.

The engineer will integrate Azure AI services with modern DevOps practices to ensure performance, reliability, and governance-compliant operations across enterprise workloads.

Qualifications

• Experience designing and deploying AI/ML on Azure.
• Deep knowledge of Azure Databricks serverless architecture.
• Hands-on with serverless tech: Functions, Event Grid, Logic Apps.
• Proficiency in Python, PySpark, and SQL for data engineering.
• CI/CD, MLOps, IaC using Azure DevOps and Terraform.
• Knowledge of cloud cost management and workload optimization.
• Understanding enterprise security, data protection, and compliance.
• Ability to design observability and monitoring for Databricks Serverless.

Responsibilities

• Designing and assessing serverless compute architectures for AI workloads.
• Implementing scalable Azure Databricks Serverless environments.
• Developing and maintaining CI/CD pipelines in Azure DevOps for AI and Databricks workloads.
• Ensuring reliability, observability, monitoring, and optimization of Databricks and serverless services.
• Collaborating closely with Data Science, Cloud Engineering, and DevOps teams as an independent expert—providing consultancy, technical alignment, and best practices for operationalizing AI models efficiently.

Skills

Azure Databricks

Python / PySpark / SQL

CI/CD / MLOps

Terraform / IaC

Azure Functions / Event Grid / Logic

Observability & Monitoring

Security & Compliance

Serverless Architectures

Tools

Azure DevOps

Terraform

Databricks

Job description

As part of a large-scale platform modernization journey, the organization is strengthening its AI and Machine Learning capabilities to deliver intelligent, secure, and scalable digital solutions on Microsoft Azure. This transformation focuses on embedding enterprise-grade AI capabilities within the platform engineering landscape—enabling automation, improving data security, and accelerating outcomes through responsible AI adoption.

To support this initiative, we are seeking an experienced AI Platform Engineer. This role will be pivotal in designing, building, and operationalizing a central AI Platform and its supporting capabilities, including AI SDK development, data masking, PII detection, and document intelligence. The engineer will integrate Azure AI services with modern DevOps practices to ensure performance, reliability, and compliance with enterprise governance standards.

Responsibilities

• Designing and assessing serverless compute architectures for AI workloads.
• Implementing scalable Azure Databricks Serverless environments.
• Developing and maintaining CI/CD pipelines in Azure DevOps for AI and Databricks workloads.
• Ensuring reliability, observability, monitoring, and optimization of Databricks and serverless services.
• Collaborating closely with Data Science, Cloud Engineering, and DevOps teams as an independent expert—providing consultancy, technical alignment, and best practices for operationalizing AI models efficiently.

Required Experience & Skills

• Proven experience designing and deploying AI/ML and data engineering solutions on Azure.
• Deep understanding of Azure Databricks architecture, including serverless compute capabilities.
• Hands-on experience with serverless technologies such as Azure Functions, Event Grid, and Logic Apps.
• Proficiency in Python, PySpark, and SQL for data engineering and model integration.
• Strong knowledge of CI/CD, MLOps, and Infrastructure-as-Code using Azure DevOps and Terraform.
• Solid understanding of cloud cost management, scaling strategies, and workload optimization.
• Familiarity with enterprise security, data protection, and compliance requirements for AI workloads.
• Ability to design and implement observability, monitoring, and alerting for Databricks Serverless environments.

Nice to have

• Experience integrating Mistral AI large language models (LLMs) into a corporate fintech environment.