MLOps Engineer – Azure & AI/ML Platforms (m/f/d)

🚀 Become our new MLOps Engineer (m/f/d)

At KI Performance, we move AI from experimentation to production. As a MLOps Engineer, you will design, build, and operate highly scalable, secure Azure-based AI platforms used in production environments. This role sits at the intersection of cloud infrastructure, DevOps, and AI delivery, with a strong focus on enabling iterative AI development, reliable model deployment, and platform scalability.

You will work closely with AI engineers, data teams, and product stakeholders to ensure that AI use cases can be developed, deployed, and operated efficiently at scale — with production-grade reliability, security, and observability.

• Design, implement, and operate scalable Azure infrastructure for AI and data-intensive platforms using Terraform

• Build and maintain secure Azure networking architectures (VNETs, subnets, NSGs, Private Endpoints)

• Implement access control and governance using Azure RBAC, Key Vault, and Azure Policies

• Ensure infrastructure is production-ready with a focus on performance, reliability, and scalability

• Design and operate modern CI/CD pipelines using GitHub Actions

• Enable fast, safe, and repeatable deployments for infrastructure, services, and AI models

• Support iterative development with strong versioning, testing, and rollback strategies

• Operationalize AI use cases using MLflow (experiment tracking, model registry, deployment workflows)

• Support the full AI lifecycle from experimentation to production deployment

• Deploy and operate model inference services exposed via REST APIs (FastAPI preferred)

• Collaborate closely with AI engineers to ensure models are production-ready

• Implement end-to-end observability using OpenTelemetry

• Set up monitoring and logging using Azure Application Insights (or equivalent tooling)

• Proactively improve system reliability, performance, and incident response

• Use Python for automation, AI integration, backend services, and tooling

• Support platform self-service capabilities for engineering teams

• Continuously improve infrastructure and operational maturity