We're looking for a passionate MLOps Engineer to join our innovative Data Science team. 🚀
You'll be the crucial link between our machine learning models and our production environment, responsible for building the infrastructure that allows our data scientists to create and deploy cutting-edge solutions at scale.
In this role, you won't just be deploying models; you'll be building and automating the entire ML lifecycle. If you love solving complex problems and want to productionize state-of-the-art AI, this is the perfect opportunity for you.
Key Responsibilities
- Design and Build ML Infrastructure: Create, manage, and scale the infrastructure required for training and deploying our machine learning models.
- Automate ML Pipelines: Develop and maintain robust CI/CD/CT (Continuous Integration/Continuous Delivery/Continuous Training) pipelines for the full ML lifecycle.
- Deploy & Serve Models: Implement strategies for deploying models as scalable, reliable services using technologies like containerization (Docker, Kubernetes) and serverless functions.
- Monitor Model Performance: Establish and manage comprehensive monitoring solutions to track model accuracy, data drift, and system health to ensure our models perform as expected in production.
- Collaborate Cross-Functionally: Work closely with data scientists to understand model requirements and with software engineers to integrate ML models into our core products.
- Champion Best Practices: Advocate for and implement MLOps best practices in versioning (data, code, models), testing, and security across the team.
