Machine Learning Engineer - MLOps

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. 

Who you are

• 4+ years of professional experience as a DevOps Engineer, MLOps Engineer, ML Engineer, Data Engineer 
• You have strong software development skills, including: version control (e.g. Git and preferable on Gitlab), coding best practices, debugging, unit and integration testing.
• You have experience with the full machine learning model lifecycle in production flows.
• You have great knowledge of MLOps architectures and practices.
• You are very proficient in python
• You have a very good understanding of software engineering practices
• You have a good understanding of machine learning algorithms, and specifically dependencies on engineering and their lifecycle.
• You have the ability to diagnose and resolve model performance, scalability, and deployment issues.
• You have strong familiarity with the standard data science toolkit, such as (py)spark, MLFlow or similar MLOps frameworks, Airflow
• You have an experimental mindset with a launch fast and iterate mentality.
• Team player with strong communication skills, and are able to convey complex outcomes to a wide range of audiences.
• You are adept at collaborating with cross-functional teams and driving best practices.

Desirable additional qualifications:

• Proficiency with observability tools, such as: Prometheus, Logsearch, Kibana and Grafana.
• Experience in platform engineering and k8s, argoCD, helm is a plus
• Knowledge of kafka or any other streaming framework is a plus
• Knowledge of front end and/or java is a plus
• Experience in working with feature store is a plus
• Experience with setting up and managing GPUs for Accelerated Deep Learning is a bonus