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.

Employment type
Full-time

Work arrangement
Yes