DevOps Engineer - AI

IFS

17h•Hybrid

About The Position

As a DevOps Engineer - AI Platform & MLOps, you will design, implement, and manage 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 help ensure AI services are secure, scalable, reliable, and production-ready. The role covers AI platform engineering, MLOps and deployment automation, cloud security and governance, and platform reliability and operations. Success is measured by AI platform reliability and availability, deployment frequency and release quality, infrastructure automation coverage, security and compliance adherence, fewer operational incidents and shorter recovery times, and improvements to AI delivery efficiency. The position offers the opportunity to build enterprise-scale AI solutions, work with generative AI technologies, and contribute to the future of AI-powered enterprise software within a collaborative global technology organization. IFS offers hybrid work opportunities and flexibility, while also valuing inclusive workplace experiences and connections with colleagues.

Requirements

At least 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 a related technical field.
Hands-on experience with Microsoft Azure.
Strong experience with Azure DevOps.
Strong experience with Bitbucket.
Strong experience with Terraform and/or Bicep.
Strong experience with Docker.
Strong experience with Kubernetes; AKS is preferred.
Experience implementing CI/CD pipelines.
Experience with monitoring tools such as Azure Monitor, Application Insights, Grafana, or Prometheus.
Strong scripting skills in PowerShell, Python, or Bash.

Nice To Haves

A Master's degree is an added advantage.
Experience with Azure AI Foundry, Azure OpenAI Service, MLOps, model deployment and monitoring, vector databases and retrieval systems, LLM application deployment and operations, or AI governance and operational monitoring.
Experience with identity and access management using Microsoft Entra ID.
Experience with secrets management using Azure Key Vault.
Experience with security scanning and vulnerability management.
Experience working in regulated or enterprise environments.

Responsibilities

Design, build, and maintain cloud-native infrastructure that supports AI workloads.
Manage AI platform environments across development, test, UAT, and production.
Implement Infrastructure as Code using tools such as Terraform and Bicep.
Optimize infrastructure performance, costs, resilience, and availability.
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.
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.
Establish monitoring, alerting, logging, and observability frameworks.
Troubleshoot production incidents and perform root-cause analysis.
Define and monitor platform SLAs, SLOs, and operational metrics.
Continuously improve platform stability and performance.
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.