Machine Learning Engineer - MLOps & Infrastructure Specialist - Germany - Remote
We are looking for a highly skilled Machine Learning Engineers with deep expertise in MLOps, model deployment, and production-grade machine learning systems .
In this role, you'll bridge the gap between data science and engineering to build robust, scalable ML pipelines and infrastructure that deliver real business value.
You will collaborate with data scientists, software engineers, and DevOps to ensure models move smoothly from experimentation to production, operating reliably at scale.
Key Responsibilities
* Design, build, and maintain end-to-end machine learning pipelines, from data ingestion to model deployment and monitoring.
* Develop and manage CI/CD pipelines for ML model training, testing, and deployment (e.g., GitHub Actions, Jenkins).
* Implement model serving and orchestration using tools like MLflow, Kubeflow, Airflow, Docker, Kubernetes, or Triton .
* Integrate feature stores (e.g., Feast, Tecton) to manage and serve features consistently.
* Monitor deployed models for drift, performance degradation, and operational issues using tools like Prometheus, Grafana, Seldon, or Evidently AI .
* Collaborate closely with data engineers and data scientists to ensure data quality and pipeline reliability.
* Optimize model inference performance for latency, throughput, and cost in production environments.
* Maintain documentation, reproducibility, and version control of datasets, models, and pipelines.
Required Qualifications
* 5+ years of experience in machine learning engineering or MLOps roles.
* Strong software engineering background (e.g., Python, Bash, REST APIs, containerization).
* Experience with MLOps tools and practices, including:
* CI/CD : GitHub Actions, GitLab CI/CD, Jenkins
* Model Serving : TensorFlow Serving, TorchServe, Triton
* Pipelines : Kubeflow, Airflow, MLflow
* Orchestration & Infrastructure : Docker, Kubernetes, Terraform
* Monitoring : Prometheus, Grafana, Seldon Core, Evidently AI
* Feature Stores : Feast, Tecton
* Experience deploying models to cloud platforms such as AWS, GCP, or Azure.
* Deep understanding of data pipelines, ETL/ELT processes, and production data systems.
* Strong problem-solving and debugging skills in distributed computing environments.
Nice to Have
* Experience with real-time inference and streaming systems (e.g., Kafka, Spark, Flink).
* Familiarity with model governance, auditability, and responsible AI practices.
* Exposure to large language models (LLMs), vector databases, and retrieval-augmented generation (RAG) pipelines.