We are looking for an experienced ML Engineer / MLOps Engineer to join a cloud-native project for a German customer. The role is strongly engineering-focused and involves building production-grade ML infrastructure, working with GPU workloads, ML pipelines, LLMs and large-scale data processing.
🏢 About Intetics
Intetics is a company that provides innovative IT and consulting services, focusing on cutting-edge technologies and solutions for global clients.
🎯 The Role
You will be working on building and orchestrating ML pipelines using Kubeflow Pipelines (KFP v2), training ML models on GPUs, fine-tuning transformers and LLMs, and more. The role requires strong Python engineering skills and solid SQL experience.
✅ Key Responsibilities
Build and orchestrate ML pipelines using Kubeflow Pipelines (KFP v2)
Train ML models on GPUs and manage GPU resources within Kubernetes
Fine-tune transformers and LLMs
Track experiments and models using MLflow
Build classical ML models with XGBoost and CatBoost
Process large datasets using SQL Server and DuckDB
Develop Python-based pipelines, integrations and tooling
Maintain high engineering standards through testing, clean code and CI/CD with GitLab CI
Work in a secure, zero-trust / secure-by-default environment with network policies and restrictive container permissions
📌 Required Qualifications
Hands-on experience with Kubeflow Pipelines, ideally KFP v2
Experience training models on GPUs
Practical experience with LLM / transformer fine-tuning
Experience with MLflow
Strong knowledge of XGBoost, CatBoost or similar boosting models
Strong Python engineering skills
Solid SQL experience and understanding of large-scale data processing
Experience with CI/CD, clean code and automated testing
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