Machine Learning Engineer

Role overview Build and scale the infrastructure that takes machine learning models from notebook to production within a global payments platform. This mid-level role owns the MLOps foundation, automates the model lifecycle, integrates streaming data pipelines, and brings agentic capabilities into ML systems — for someone who cares as much about reliability and observability as model performance.

Responsibilities - Design, build, and maintain the MLOps platform: experiment tracking, model registry, versioning, reproducible training pipelines, and CI/CD practices for ML with validation gates and promotion workflows - Take models from research or prototype stage to robust, scalable production services, including low-latency batch, online, and real-time inference infrastructure - Implement monitoring for model performance, data drift, and concept drift, with clear alerting and rollback paths - Automate retraining, evaluation, and deployment pipelines, including self-healing and auto-rollback mechanisms triggered by performance thresholds - Integrate ML models with streaming data platforms for real-time feature computation and inference, ensuring consistency between offline training and online serving features - Design and integrate agentic workflows (LLM-based agents, tool-calling pipelines) alongside traditional ML models, with observability, guardrails, and evaluation frameworks

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

Nice to have - Exposure to LLM and agent frameworks and evaluation practices - Experience in a regulated or high-throughput domain such as fintech, payments, or healthcare - Contributions to open source MLOps or agentic tooling - Experience with cloud ML platforms such as SageMaker, Vertex AI, or Databricks

Benefits and work setup - Remote work from anywhere - Competitive compensation plus stock options - One-time home office allowance and provided work equipment - Health plan coverage regardless of location - Flexible days off and budgets for language, professional, and personal growth courses