AI Platform Engineer

DATAGROUP

. Develop and operate a sovereign LLM platform, including an OpenAI-compatible gateway, model serving, and GPU infrastructure .

Posted 11/4/2026full-timeRemote • GermanyMid-LevelSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in developing and operating LLM platforms, with a strong focus on container-based applications, CI/CD processes, and cloud infrastructure management. Proficient in ensuring data protection and security while implementing monitoring and observability solutions.

Highest-signal resume keywords

LLM Platform DevelopmentKubernetes ManagementCI/CD Pipeline ImplementationDocker ContainerizationCloud Infrastructure Management

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

PythonPostgreSQLVector DatabasesModel ServingRAG ArchitecturesAI ServicesInfrastructure as CodeMonitoring SolutionsLogging SolutionsAlerting Solutions

Soft Skills

Independent Working StyleStructured Working StyleHigh Degree of OwnershipStrong Awareness of SecurityData Protection Awareness

Tools & Technologies

AzureDockerKubernetesLinuxCoolify

Industry Keywords

DevOpsPlatform EngineeringCloud EnvironmentsAI ServicesTenant Isolation

Tech Stack

Tools & technologies

AzureCloudDockerKubernetesLinuxPostgresPython

About the role

Key responsibilities & impact

Develop and operate a sovereign LLM platform, including an OpenAI-compatible gateway, model serving, and GPU infrastructure
Implement customer-specific knowledge integrations (RAG) using vector databases, embeddings, and reranking
Ensure cost control, tenant isolation, data protection, and guardrails
Establish monitoring and observability for the performance, quality, and usage of AI services
Further develop the container-based platform using Docker, Coolify, and Kubernetes
Automate deployments and take responsibility for CI/CD processes for in-house development projects
Manage Linux, cloud, and database infrastructure while ensuring stability, security, and availability
Build and enhance monitoring, logging, and alerting solutions
Actively shape the architecture of platform and AI services
Establish Infrastructure as Code and standardized deployment processes
Ensure data protection-compliant and tenant-secure operating models
Document solutions, share knowledge within the team, and evaluate new technologies in platform engineering and AI

Requirements

What you’ll need

Degree in computer science, business informatics, or a comparable qualification
Several years of experience in DevOps, platform engineering, or cloud/infrastructure environments
Solid hands-on experience operating container-based applications and modern platforms
Experience building and operating LLM and AI services in production, ideally in self-hosted environments
Very good knowledge of Kubernetes, Docker, and Linux
Experience with CI/CD pipelines, cloud platforms—particularly Azure—and PostgreSQL
Practical experience with LLM gateways, RAG architectures, vector databases, and model-serving solutions
Solid Python skills for automation and integrations
Independent, structured working style with a high degree of ownership, along with a strong awareness of security and data protection
Strong understanding of architecture and platform engineering

Benefits

Comp & perks

Open and collaborative company culture
Trust-based working environment
Comprehensive onboarding with support from the entire AIS team
Flat hierarchies, short decision-making processes, and a high degree of freedom to shape your work
Strategic role with significant influence over the future platform landscape
Opportunity to work on sovereign LLM hosting and AI infrastructure
Modern infrastructure featuring Kubernetes, Docker, CI/CD, and cloud technologies
Opportunity to actively shape standards, processes, and architectural decisions
Individual professional development budget for conferences, certifications, and specialized training
Clear career and development paths
Opportunity to specialize as an AI Platform Engineer or LLMOps expert
Small, focused, and agile team with short decision-making processes
Open feedback culture with regular one-to-one meetings and retrospectives