MLOps Engineer (ML Workflows Engineering) (m/f/d)
Core
Designing tools, automation, and pipelines to streamline machine learning operations (MLOps) and enable the development of ML models and intelligent agents within developer IDEs.
Role type
MLOps Engineer (ML Workflows Engineering)
Builds
End-to-end machine learning pipelines, monitoring/logging/tracing systems, and automation tools for GPU clusters.
Domain
Developer Tools / Artificial Intelligence / MLOps
Deliverable
production ML models | infrastructure
Required skills
MLOps tooling, Kubernetes, Cloud providers (GCP, AWS), ML orchestration frameworks, CI/CD systems, Python, distributed systems
Preferred skills
ML orchestrators (ZenML, Dagster, Airflow), Python backend services, Experiment tracking (Weights & Biases, MLflow, Langfuse), LLM inference frameworks (vLLM, DeepSpeed, TensorRT), NLP theory, Java/Kotlin
Technologies
Kubernetes, GCP, AWS, GitHub Actions, JetBrains TeamCity, ZenML, Dagster, Airflow, Weights & Biases, MLflow, Langfuse, vLLM, DeepSpeed, TensorRT
Responsibilities
Build tools and workflows to simplify infrastructure-heavy tasks; Develop robust monitoring and tracing systems; Design and maintain end-to-end ML pipelines; Work with large-scale distributed systems and GPU clusters; Optimize workflows for reproducibility, scalability, and cost-efficiency.
Seniority
Mid-Senior, hands-on IC