Das ist der Job
Build and maintain CI/CD for ML (testing, packaging, versioning, reproducibility, automated rollbacks, approvals).
Darum lohnt es sich
You’ll design reliable, secure, and compliant systems for model development, evaluation, deployment, monitoring, and continuous improvement—working closely with ML, data, security, and product teams.
Position Summary
We’re hiring a Senior MLOps Engineer with deep machine learning engineering experience to build and operate the production platform powering ML/LLM-driven healthcare workflows.
This role is ideal for someone who has shipped ML systems in production and is excited about LLM orchestration, RAG, evaluations, guardrails, and observability in a regulated environment.
Key responsibilities MLOps & ML Platform Design and operate ML platforms that support end-to-end workflows: data ingestion, feature engineering, training, evaluation, deployment, and monitoring. Implement MLOps best practices: model registry, experiment tracking, lineage, governance, and reproducible training environments.
Develop scalable training infrastructure (distributed training, GPU scheduling, cost controls, auto-scaling). Create and maintain feature pipelines / feature stores, ensuring consistency between training and inference (training-serving skew prevention).
Establish model monitoring and observability: performance, drift, bias/fairness signals (where relevant), latency, t
