Join Yuno as a Machine Learning Engineer to build and scale MLOps infrastructure for intelligent transaction routing and fraud prevention.
Posted by employer 1 day ago
First seen on Joblaze 58 minutes ago
Last verified on the company career page 58 minutes ago
Not disclosed in this posting: compensation, work arrangement, visa sponsorship.
Joblaze summary
In this role, the Machine Learning Engineer is responsible for developing and scaling the infrastructure that transitions machine learning models from development to production. Key skills include expertise in MLOps, automation of model lifecycles, and integration of streaming data pipelines, with a focus on both reliability and performance. This position is suited for mid-level professionals with 5 to 8 years of experience, particularly those with a background in data science or software engineering. The engineer will collaborate closely with various teams, enhancing Yuno's AI-driven payment solutions.
Quick facts
- How much experience is required?
- 5–8 years of relevant experience for this Machine Learning Engineer role.
- What's the tech stack?
- Joblaze extracted these technologies from the posting: AI, MLOps, Machine Learning.
- What seniority level is this role?
- Yuno targets mid-level candidates for this position.
- Is this full-time or contract?
- Full-time for this Machine Learning Engineer role at Yuno.
From the original posting
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.