MLOps Engineer

Team: IT

Location: Germany

Commitment: Full-time

Workplace Type: remote

Accountabilities

• Develop, automate, and maintain scalable machine learning pipelines, CI/CD workflows, and orchestration frameworks to support efficient model development and deployment.
• Design and implement high-performance model serving infrastructure using industry-standard serving frameworks while optimizing inference for low latency and high throughput.
• Build reliable deployment strategies including A/B testing, canary releases, rollback mechanisms, and production validation processes.
• Create robust monitoring, logging, alerting, and observability solutions to ensure model reliability, performance, and operational excellence.
• Optimize infrastructure utilization by improving GPU efficiency, enabling autoscaling, and managing cloud resources effectively.
• Design and maintain feature stores, scalable data pipelines, and storage architectures capable of supporting large-scale training and inference workloads.
• Collaborate with engineering teams to continuously improve platform scalability, security, governance, and operational best practices.

Requirements

• Bachelors or Masters degree in Computer Science, Engineering, or a related discipline, or equivalent practical experience.
• At least 5 years of experience in MLOps, DevOps, or related engineering roles supporting production machine learning environments.
• Proven experience designing and building MLOps infrastructure from the ground up using platforms such as MLflow, Weights & Biases, Kubeflow, or similar.
• Strong hands-on experience with machine learning frameworks including PyTorch and TensorFlow, as well as model serving technologies such as TorchServe, TensorFlow Serving, Triton, or KServe.
• Solid experience developing and managing scalable data pipelines, Kubernetes environments, cloud infrastructure (AWS, GCP, or Azure), and Infrastructure as Code solutions including Terraform, Helm, or GitOps.
• Strong programming skills in Python, Bash, and Go, with a focus on maintainable, scalable, and production-quality software.
• Knowledge of AI system security, model governance, compliance, monitoring, and observability tools such as Prometheus, Grafana, Datadog, or OpenTelemetry.
• Experience with FastAPI, Databricks, Snowflake, SRE practices, or cloud security certifications is considered an advantage.

Benefits

• Competitive salary and equity package.
• Comprehensive healthcare coverage for employees and eligible dependents.
• Paid parental leave supporting all paths to parenthood, including adoption and surrogacy.
• Relocation assistance for employees joining one of the companys office locations where applicable.
• Fully remote work within Europe.
• Opportunity to work on cutting-edge AI technologies with significant technical ownership.
• Inclusive, collaborative, and mission-driven engineering culture focused on innovation, learning, and professional growth.