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Yuno is seeking a Machine Learning Engineer to design, build, and maintain a comprehensive MLOps platform and production ML infrastructure. This is a senior individual contributor role focused on bridging the gap between ML research and robust, scalable production systems.
Key Responsibilities:
MLOps Framework: Design and build 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 across teams.
ML Productionization: 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 comprehensive 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 around latency, cost, and scale.
Automation & Self-Healing: 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 enables ML practitioners to ship models without requiring deep infrastructure 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: Design and integrate agentic workflows (LLM-based agents, tool-calling pipelines) alongside traditional ML models. Build observability, guardrails, and evaluation frameworks for reliable production agentic systems. Explore how agents can automate parts of the ML lifecycle itself (monitoring, triage, retraining decisions).
The role is fully remote within Europe, with competitive compensation, stock options, home office setup allowance, flexible time off, and professional development support.
Requirements:
- 5–8 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)
- Experience with 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 regulated or high-throughput domains (fintech, payments, healthcare)
- Contributions to open-source MLOps or agentic tooling
- Experience with cloud ML platforms (SageMaker, Vertex AI, Databricks)
