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Teya is a Series C fintech company building a financial platform for local businesses across Europe. The company focuses on simplifying financial services for cafés, restaurants, salons, shops, and entrepreneurs through thoughtful design, reliable tools, and human-centered support.
You will join the Data team as a Senior ML Engineer responsible for developing and maintaining Teya's ML platform. This is a technical leadership role focused on platform architecture, data product thinking, and engineering best practices that enable both analytics and future AI capabilities.
Key responsibilities include:
- Develop and maintain the platform, services, and tooling used to deploy, serve, and operate machine learning models in production
- Deploy and operate ML workloads across the ML infrastructure
- Improve automation across the ML lifecycle: model packaging, deployment, versioning, monitoring, and release processes
- Maintain and improve reliability and observability of the ML platform and model-serving services (logging, metrics, alerting)
- Participate in on-call rotation, investigate production incidents, and drive preventive improvements
- Collaborate with data scientists, software engineers, and platform teams to turn ML use cases into reliable production solutions
- Participate in technical discussions and code reviews, contributing to maintainable designs and strong engineering standards
- Create and maintain technical documentation and operational runbooks
You will work in a fast-moving environment that values quality, attention to detail, and genuine hospitality alongside strong performance.
REQUIREMENTS:
- 5+ years' experience in ML Engineering, MLOps, or similar roles
- Proficiency in Python
- Experience with managed ML platforms (e.g., Amazon SageMaker or equivalent)
- Practical knowledge of deploying and operating containerized workloads (Docker, Kubernetes)
- Experience developing or operating model-serving platforms, inference services, or backend APIs
- Familiarity with feature-store concepts (feature discovery, reuse, versioning, online/offline consistency)
- Familiarity with model registries, experiment tracking, and ML metadata management
- Experience with performance, scalability, and reliability considerations for real-time systems
- Hands-on experience provisioning and managing cloud infrastructure with Terraform
- Experience with CI/CD pipelines, automated testing, and Git-based development workflows
- Familiarity with observability practices (logging, metrics, alerting, production troubleshooting)
- Strong grasp of software engineering principles and best practices
- Experience contributing to or leading data warehouse architecture or redesign initiatives
- Ability to collaborate effectively with technical and non-technical stakeholders
