ML/MLOps Engineer

Quick Facts

• Role: ML/MLOps Engineer

• Project: Cloud-native AI platform and scalable ML solutions for the healthcare industry

• Focus: Building an MLOps platform (Kubeflow-based) for large-scale, secure ML/AI workloads

Description

You will help build and orchestrate a cloud-native MLOps platform for healthcare, supporting GPU training, LLM fine-tuning, and scalable data processing in a zero-trust environment. The work includes creating ML pipelines, experiment tracking, and integrating both transformer-based and classic ML models. You will also ensure strong engineering practices through testing and CI/CD.

Responsibilities

• Build and orchestrate ML pipelines using Kubeflow Pipelines (KFP v2)

• Train models on GPUs with Kubernetes-based GPU resource management

• Fine-tune transformers/LLMs and track experiments/models with MLflow

• Develop classic ML models using XGBoost and CatBoost

• Process large datasets in-cluster using SQL Server and DuckDB

• Develop in Python using pipelines and integrations with modern tooling (uv)

• Maintain code quality with testing and GitLab CI (CI/CD)

• Work in a secure-by-default / zero-trust environment using network policies and restrictive container rights

Requirements

• 5+ years of experience as an MLOps Engineer / ML Engineer

• Hands-on experience with Kubeflow Pipelines (KFP v2)

• Experience training models on GPUs

• Experience fine-tuning LLMs/transformers

• Experience with MLflow (model tracking)

• Experience with boosting models (XGBoost, CatBoost)

• Deep proficiency in Python and modern engineering practices

• Experience in regulated/enterprise cloud-native environments

• Experience with SQL and large-scale data processing

• CI/CD experience (GitLab CI preferred), clean code and testing practices

• English level: Intermediate+ or above

• German level: Intermediate+ or above

Benefits

• Remote, office, or hybrid work options

• Mentoring and onboarding support for each new employee

• Professional, financial, and career growth

• Annual bonus with potential additional up to $1,000/month based on expertise

• Access to a corporate training portal

• Certification compensation (e.g., AWS, PMP)

• Private health insurance and sports activity compensation

• Referral program and supportive corporate culture