About the job

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.