MLOps Engineer – Azure & AI/ML Platforms (all genders)

🚀 Become our new MLOps Engineer (all genders)

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.

Your responsibilities

Cloud Infrastructure & Platform Engineering

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

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

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

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

CI/CD & Release Engineering

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Design and operate modern CI/CD pipelines using GitHub Actions

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

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

MLOps & AI Platform Enablement

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

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Support the full AI lifecycle from experimentation to production deployment

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

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

Observability & Reliability

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Implement end-to-end observability using OpenTelemetry

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

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Proactively improve system reliability, performance, and incident response

Engineering & Automation

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

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Support platform self-service capabilities for engineering teams

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Continuously improve infrastructure and operational maturity