Machine Learning Engineer

Your Contribution Will Be

• MLOps framework: design, build and maintain the MLOps platform (experiment tracking, model registry, versioning and reproducible training pipelines); establish CI/CD practices for ML with automated testing, validation gates and promotion workflows from dev to production; define standards and tooling for feature stores, model artifacts and environment reproducibility across teams.
• ML productionizing: take models from research or prototype stage to robust, scalable production services; build low latency, high availability serving infrastructure (batch, online and real time inference); implement monitoring for model performance, data drift and concept drift, with clear alerting and rollback paths; partner with data science teams to harden models for production constraints (latency, cost, scale).
• Automation of ML models: automate retraining, evaluation and deployment pipelines to reduce manual intervention; build self healing and auto rollback mechanisms triggered by performance or drift thresholds; create tooling that lets ML practitioners ship models without needing deep infra expertise.
• Streaming integration: integrate ML models with streaming data platforms (Kafka, Kinesis, Flink) for real time feature computation and inference; design low latency feature pipelines that bridge batch and streaming data sources; ensure consistency between offline (training) and online (serving) feature computation.
• Agentic integration for ML: design and integrate agentic workflows (LLM based agents, tool calling pipelines) alongside traditional ML models; build the observability, guardrails and evaluation frameworks needed to run agentic systems reliably in production; explore how agents can automate parts of the ML lifecycle itself (monitoring, triage, retraining decisions).

Minimum Qualifications

• 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.

Preferred Qualifications

• Exposure to LLM and agent frameworks and evaluation practices.
• Experience in a regulated or high throughput domain (fintech, payments, healthcare).
• Contributions to open source MLOps or agentic tooling.
• Experience with cloud ML platforms (SageMaker, Vertex AI, Databricks).

What We Offer at Yuno

• 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