About the role:

Billie is proud of having its decision engine, consisting of a combination of cutting-edge machine learning models and business logics, built internally. This engine is a core functionality of the product allowing Billie to evaluate Fraud and Risk propensity efficiently.
As a Senior MLOps Engineer, you will be responsible for designing, building, and maintaining scalable and resilient machine learning pipelines that support real-time and batch model deployment and monitoring. Your expertise in ML model lifecycle management, CI/CD for machine learning, and MLOps best practices will be key in supporting various AI and machine learning initiatives across the organization.

In this job you will:

• Design and implement a scalable and reliable ML platform: Develop robust ML pipelines for real-time and batch processing using tools such as MLflow, Kubeflow, Metaflow
• Ensure that ML model predictions are delivered reliably and timely and are continuously monitored for performance, accuracy, and reliability.
• Maintain and extend our inhouse batch and real-time feature platforms that power dozens of feature pipelines allowing timely data for model inference and reliable point in time accurate data for model training
• Collaborate with cross-functional teams: Work closely with data scientists and data engineers to understand model and feature requirements and translate them into scalable and reliable pipelines.
• Drive continuous improvement: Actively contribute to the team's knowledge sharing, participate in tech talks, and drive architectural decisions as the subject matter expert (SME) in MLOps.
• Stay abreast of industry trends and advancements in MLOps technologies to improve our ML Platform

Who we are looking for:

• You are familiar with data versioning and data governance practices
• You have an understanding of model versioning, reproducibility, and Experimentation using tools like MLflow or Weights & Biases
• You have experience with machine learning workflows and MLOps tools such as Metaflow and MLflow
• You have knowledge of model deployment, serving, and monitoring using tools like AWS SageMaker, KServe, Seldon Core
• You are familiar with model training and inference using popular frameworks like Scikit-learn or (optional) TensorFlow, PyTorch
• You are able to design and implement model deployment pipelines using Github Actions, ArgoCD or similar
• You have experience using orchestration tools such as Airflow, ArgoCD, Prefect.
• You are able to collaborate with data scientists to deploy, monitor, and optimize ML models in production