Machine Learning Engineer

• Design, build and maintain the MLOps platform, including experiment tracking, model registry, versioning and reproducible training pipelines.
• Establish CI/CD practices for ML with automated testing, validation gates and promotion workflows from development to production.
• Define standards and tooling for feature stores, model artifacts and environment reproducibility.
• Take models from research or prototype stage to robust, scalable production services.
• Build low-latency, high-availability serving infrastructure for batch, online and real-time inference.
• Implement monitoring for model performance, data drift and concept drift, with alerting and rollback paths.
• Partner with data science teams to harden models for production constraints such as latency, cost and scale.
• Automate retraining, evaluation and deployment pipelines.
• Build self-healing and auto-rollback mechanisms.
• Create tooling enabling ML practitioners to ship models without deep infrastructure expertise.
• Integrate ML models with Kafka, Kinesis or Flink for real-time feature computation and inference.
• Design low-latency feature pipelines bridging batch and streaming data sources.
• Ensure consistency between offline training and online serving feature computation.
• Design and integrate agentic workflows, including LLM-based agents and tool-calling pipelines, alongside traditional ML models.
• Build observability, guardrails and evaluation frameworks for reliable production agentic systems.
• Explore agents automating parts of the ML lifecycle, including monitoring, triage and retraining decisions.
• Work closely with data science, platform and product teams.

• 5 to 8 years of experience in ML engineering, MLOps or backend infrastructure with ML systems in production.
• Strong software engineering fundamentals; comfortable owning services end to end.
• Experience with model serving frameworks (Seldon, KServe, BentoML, TorchServe or similar) and orchestration tools (Airflow, Kubeflow, MLflow or similar).
• Hands on experience with streaming systems (Kafka, Kinesis, Flink or similar).
• Familiarity with containerization and orchestration (Docker, Kubernetes).
• Experience with observability tooling (metrics, tracing, logging) for ML or distributed systems.
• Strong communication skills and comfort working cross functionally with data science, platform and product teams.
• Fluent English.
• Based in Europe.

• Competitive Compensation
• Remote Work: you can work from everywhere
• Home Office Bonus: a one time allowance to set up your ideal home office
• Work Equipment
• Stock Options
• Health Plan wherever you are
• Flexible Days Off
• Language, Professional, and Personal Growth courses