Manager - AI Engineering

A results-driven AI Engineer with strong DevOps and Cloud Engineering expertise to support the design, deployment, and operation of scalable, production-grade AI and data platforms on Microsoft Azure. The ideal candidate brings hands-on experience working at the intersection of DevOps, cloud infrastructure, and machine learning enablement, with a clear focus on automation, reliability, and secure delivery.

• 3 to 6 years of relevant experience in AI platform enablement, DevOps, Cloud Engineering, or related roles
• Proven experience supporting production workloads on Microsoft Azure
• Hands-on exposure to CI/CD automation, container platforms, and cloud-native architectures

Education

• Bachelor's degree required (Engineering, Computer Science, or related discipline preferred)
• Technical Skills
• Azure DevOps Pipelines and GitHub Actions
• YAML-based CI/CD automation
• Azure Kubernetes Service (AKS)
• Docker and Helm
• Azure App Service, Virtual Machines, VNet
• Azure Data Lake Storage (ADLS Gen2)
• Azure SQL Database
• Azure Container Registry (ACR)
• Azure Key Vault
• Azure Data Factory and Databricks
• Azure Monitor and Log Analytics

• Bash, PowerShell, Python (basic)
• Professional Skills
• Strong problem-solving and analytical mindset
• Excellent collaboration and stakeholder communication skills
• Experience working in Agile / Scrum teams
• High ownership, accountability, and execution focus
• Ability to work across DevOps, data, and AI engineering domains

• Continuous improvement and automation-first mindset

Key Responsibilities

• Build, manage, and optimize CI/CD pipelines using Azure DevOps and GitHub Actions for AI, data, and application workloads.
• Deploy and operate AI-enabled and data platforms on Azure Kubernetes Service (AKS) using Docker and Helm.
• Provision and manage Azure infrastructure including compute, networking, storage, and security services.
• Enable MLOps and data pipelines by supporting ETL workflows using Azure Data Factory and Databricks.
• Implement secure configuration and secrets management using Azure Key Vault.
• Monitor platform health, performance, and availability using Azure Monitor and Log Analytics.
• Conduct performance and load testing using JMeter / BlazeMeter and drive optimization actions.
• Collaborate with ML engineers to support model packaging, versioning, deployment, and monitoring in production.
• Support Agile delivery through automation, release coordination, and continuous improvement.
• Track and optimize cloud infrastructure costs across environments.