DevOps Engineer – AI

Role Overview

As a DevOps Engineer – AI Platform & MLOps, you will be responsible for designing, implementing, and managing the cloud infrastructure, CI/CD pipelines, deployment automation, and operational excellence practices that support AI and machine learning solutions. You will work closely with AI Engineers, Data Scientists, Solution Architects, and Platform Engineers to ensure AI services are secure, scalable, reliable, and production-ready.

Key Responsibilities

AI Platform Engineering

• Design, build, and maintain cloud-native infrastructure supporting AI workloads.
• Manage AI platform environments across development, test, UAT, and production.
• Implement Infrastructure as Code (IaC) using tools such as Terraform and Bicep.
• Optimize infrastructure performance, costs, resilience, and availability.

MLOps & Deployment Automation

• Build and maintain CI/CD pipelines for AI and machine learning solutions.
• Automate model deployment, validation, testing, and rollback processes.
• Support model lifecycle management, versioning, monitoring, and governance.
• Enable automated retraining and deployment workflows where required.

Cloud & Security

• Implement security best practices across AI environments.
• Manage identities, secrets, certificates, and access controls.
• Ensure compliance with organizational security and governance standards.
• Conduct vulnerability remediation and platform hardening activities.

Platform Reliability & Operations

• Establish monitoring, alerting, logging, and observability frameworks.
• Troubleshoot production incidents and perform root-cause analysis.
• Define and monitor platform SLAs, SLOs, and operational metrics.
• Drive continuous improvement of platform stability and performance.

Collaboration & Engineering Excellence

• Partner with AI Engineers and Solution Architects to operationalize AI solutions.
• Support production releases and deployment readiness reviews.
• Contribute to platform standards, reusable templates, and automation frameworks.
• Mentor development teams on DevOps and AI operational best practices.

AI / MLOps Experience

Experience in one or more of the following:

• Azure AI Foundry
• Azure OpenAI Service
• Machine Learning Operations (MLOps)
• Model deployment and monitoring
• Vector databases and retrieval systems
• LLM application deployment and operations
• AI governance and operational monitoring

Security & Governance

• Identity and access management using Microsoft Entra ID.
• Secrets management using Azure Key Vault.
• Security scanning and vulnerability management.
• Experience working in regulated or enterprise environments.

Success Measures

Success in this role will be measured through:

• Reliability and availability of AI platforms.
• Deployment frequency and release quality.
• Infrastructure automation coverage.
• Security and compliance adherence.
• Reduction of operational incidents and recovery times.
• Continuous improvement of AI delivery efficiency.

What We Offer

• Opportunity to build enterprise-scale AI solutions.
• Exposure to the latest Generative AI technologies.
• Collaborative and innovation-driven engineering culture.
• Career growth within a global technology organization.
• The chance to help shape the future of AI-powered enterprise software.

Required Skills & Experience

• 3+ years of experience in DevOps, Platform Engineering, Site Reliability Engineering, or a similar role.
• Bachelor’s degree in computer science, Software Engineering, Data Science, Mathematics, Statistics, or related technical field. Masters will be an added advantage
• Hands-on experience with Microsoft Azure.
• Strong experience with:
  • Azure DevOps
  • Bitbucket
  • Terraform and/or Bicep
  • Docker
  • Kubernetes (AKS preferred)
  • CI/CD pipeline implementation
• Experience with monitoring tools such as Azure Monitor, Application Insights, Grafana, or Prometheus.
• Strong scripting skills in PowerShell, Python, or Bash.