LLM Platform Engineer

XpertDirect; Berlin; Vor Ort; EUR 85.000 - 120.000; Vollzeit

Zusammenfassung

XpertDirect in Berlin is seeking an LLM Platform Engineer to build the internal platform for deploying, evaluating, observing, and operating LLM-powered applications at scale.

You will work across AI Engineering and Platform Engineering, creating shared infrastructure behind model access, retrieval, evaluation, observability, and production deployment.

The role emphasizes cloud-native systems, Kubernetes, and Terraform, with opportunities to shape internal developer platforms.

Qualifikationen

• 4+ years in Platform Engineering, AI Engineering, MLOps, or backend roles.
• Strong experience with production cloud-native systems.

Aufgaben

• Build platform services for production LLM applications.
• Develop model gateways and APIs for accessing multiple LLM providers.
• Build shared RAG and vector retrieval infrastructure.
• Develop Python tooling and services for AI engineering teams
• Deploy and operate AI workloads on Kubernetes
• Build evaluation pipelines for LLM and RAG applications
• Implement observability across prompts, models, retrieval, latency, cost, and failures
• Automate infrastructure using Terraform
• Create deployment tooling and self-service capabilities for engineering teams
• Improve reliability, scalability, and operational standards across production AI applications

Kenntnisse

Platform engineering

AI/ML engineering

Backend engineering

MLOps

Tools

Pinecone

Weaviate

Qdrant

pgvector

Jobbeschreibung

Developer Tools | AI SaaS | LLM Infrastructure | Platform Engineering

Our client, a growing AI SaaS / Developer Tools company based in Berlin, is looking for an LLM Platform Engineer to build the internal platform that enables engineering teams to deploy, evaluate, observe, and operate LLM-powered applications at scale.

You'll work across AI Engineering and Platform Engineering, creating the shared infrastructure behind model access, retrieval, evaluation, observability, and production deployment.

What You'll Work On

• Build platform services for production LLM applications
• Develop model gateways and APIs for accessing multiple LLM providers
• Build shared RAG and vector retrieval infrastructure
• Develop Python tooling and services for AI engineering teams
• Deploy and operate AI workloads on Kubernetes
• Build evaluation pipelines for LLM and RAG applications
• Implement observability across prompts, models, retrieval, latency, cost, and failures
• Automate infrastructure and environments using Terraform
• Create deployment tooling and self-service capabilities for engineering teams
• Improve reliability, scalability, and operational standards across production AI applications

Core Skills

• 4+ years in Platform Engineering, AI Engineering, MLOps, Backend Engineering, or similar roles
• Strong understanding of production cloud-native systems

Nice to Have

• Pinecone, Weaviate, Qdrant, or pgvector
• LangGraph / LangChain
• LLM gateways or routing platforms
• OpenTelemetry
• LLM evaluation and tracing tools
• AWS / GCP
• Experience building internal developer platforms