AI Platform Engineer

DATAGROUP

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

Posted 10/4/2026full-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 data protection compliance. Proficient in leveraging Kubernetes, Docker, and cloud infrastructure to ensure stability, security, and performance of AI services.

Highest-signal resume keywords

LLM Platform DevelopmentKubernetes ExpertiseCI/CD Process OwnershipContainer-Based Application ManagementData Protection Compliance

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

PythonKubernetesDockerCI/CD PipelinesPostgreSQLVector DatabasesModel ServingRAG ArchitecturesLinuxCloud Platforms

Soft Skills

Independent Working StyleStructured ApproachHigh Degree of OwnershipStrong Security Awareness

Tools & Technologies

AzureCoolifyMonitoring SolutionsLogging SolutionsAlerting Solutions

Industry Keywords

Platform EngineeringDevOpsAI ServicesSovereign LLMInfrastructure as Code

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 ownership of CI/CD processes for in-house developments
Maintain Linux, cloud, and database infrastructure while ensuring stability, security, and availability
Build and enhance monitoring, logging, and alerting solutions
Play an active role in shaping the architecture of platform and AI services
Establish Infrastructure as Code and standardized deployment processes
Ensure data protection-compliant and tenant-isolated operating models
Document solutions, share knowledge across 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
Extensive hands-on experience operating container-based applications and modern platforms
Experience building and running 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 and structured working style, a high degree of ownership, and strong awareness of security and data protection
Strong architectural and platform expertise

Benefits

Comp & perks

Open and collaborative company culture and a trusting working environment
Comprehensive onboarding with support from the entire AIS team
Flat hierarchies, short decision-making processes, and a high degree of autonomy
Strategic role with significant influence on 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