Build and scale MLOps infrastructure that moves machine learning models from development to production. Responsibilities include model serving, automated training and deployment, CI/CD, monitoring, drift detection, rollback mechanisms, streaming feature pipelines, and agentic workflow integration. The role requires collaboration with data science, platform, and product teams to deliver reliable, observable, low-latency ML systems.
Who We Are
At Yuno, we are building the payment infrastructure that allows all companies to participate in the global market. Founded by veterans from payments and technology, including people who previously built Rappi, Yuno connects companies like InDrive, McDonald's, Rappi and Viva Aerobus with 300+ payment methods worldwide via a single API. We use AI and modern technology for intelligent transaction routing and fraud prevention across more than 80 countries.
About The Role
We are looking for a Machine Learning Engineer (mid level, 5 to 8 years of experience) based in Europe to build and scale the infrastructure that takes machine learning models from notebook to production. You will own the MLOps foundation, automate the model lifecycle, integrate streaming data pipelines and bring agentic capabilities into our ML systems. This is a hands on role for someone who cares as much about reliability and observability as about model performance. You will join the Data, AI and ML team and work closely with data science, platform and product teams.
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
We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, or to exercise your data protection rights, please contact us.
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