Junior Machine Learning Ops
Role details
Job location
Tech stack
Job description
Adyen is seeking a MLOps Engineer to join our central MLOp team, which is responsible for building platforms and tools for all of our data science teams. In this role, you will play a crucial part in shaping the MLOps ecosystem at Adyen, serving a variety of machine learning and statistical models for both real-time and batch predictions - from optimizing payments to combating fraud.
Note: In this role, you won't be building machine learning models yourself. Instead, your focus will be on enabling and supporting the teams who are developing them.
What you'll will be doing:
- Own, develop, deploy and operate tooling and services around MLOps:
- Performant model training and tracking.
- Safe, stable and performant machine learning model deployment in both real-time and batch flows, considering latency, reliability and scalability.
- Experiment tracking, validation and hyperparameter optimization runs
- Model monitoring for downtime, latency, and drifts.
- Ensuring scalability of the MLOps infrastructure and bringing MLOps maturity to the next level
- Building tools to democratize machine learning practices at Adyen. Work closely with product machine learning teams to identify their pain-points, way of working.
Requirements
Do you have experience in Scalability?, * 2-4 years of professional experience in MLOps, DevOps, ML Engineering, or related fields (e.g., internships, academic projects, or relevant coursework).
- You demonstrate basic proficiency in Python, and are able to write clean, readable code with guidance. You are actively improving your skills through hands-on practice and mentorship.
- You are comfortable with Git-based version control systems (e.g., GitHub or GitLab), including branching, pull requests, and resolving merge conflicts, ideally as part of a team workflow.
- You have an understanding of software engineering practices, such as debugging, logging, and writing simple unit tests, and you are eager to learn and adopt more advanced patterns.
- You are familiar with the machine learning model lifecycle, especially in terms of model training, evaluation, and deployment steps
- You understand MLOps concepts and tools, such as model versioning, automated pipelines, and reproducibility
- You show curiosity about ML algorithms and data science workflows
- You are eager to learn from and support senior engineers, and you actively seek feedback to grow your capabilities and confidence.
- You show a collaborative mindset, communicate effectively within your team, and are comfortable asking questions or sharing ideas, even when unsure.
- You are developing an experimental and iterative mindset, and are open to trying new approaches, learning from mistakes, and working within agile workflows.