Senior Machine Learning Engineer (ML Platform)

Your Role

We are seeking a Senior ML Engineer to join our team and help shape the future of our ML platform. You will play a key role not only in maintaining, but also in helping to shape and build our growing number of ML use cases within the company, both for batch and real-time decision-making.

As a senior member of the Data team, you will contribute to platform architecture, data product thinking, and engineering best practices, helping build a world-class, scalable Data Platform that enables both analytics and future AI capabilities.

Your main responsibilities will include:

• Develop and maintain the platform, services, and tooling used to deploy, serve, and operate machine learning models in production.
• Deploy and operate machine learning workloads across our ML infrastructure.
• Improve automation across the ML lifecycle, including model packaging, deployment, versioning, monitoring, and release processes.
• Maintain and improve the reliability and observability of the ML platform and model-serving services, including logging, metrics, and alerting.
• Participate in the team's on-call rotation, investigate production incidents, and contribute to improvements that prevent them from recurring.
• 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 clear technical documentation and operational runbooks.

Teya is proud to be an equal opportunity employer.

We are committed to creating an inclusive environment where everyone regardless of race, ethnicity, gender identity or expression, sexual orientation, age, disability, religion, or background can thrive and do their best work. We believe that a diverse team leads to better ideas, stronger outcomes, and a more supportive workplace for all.

If you require any reasonable adjustments at any stage of the recruitment process whether for interviews, assessments, or other parts of the application—we encourage you to let us know. We are committed to ensuring that every candidate has a fair and accessible experience with us.

Hello. We’re Teya.

Teya was founded on a simple belief: local businesses deserve better.

They are the cafés, restaurants, salons, shops and entrepreneurs that bring character to our high streets, create jobs and keep communities moving. Yet for too long, financial services has made life harder for them - with clunky tools, poor support and complexity that gets in the way of running a business.

Teya exists to change that.

We’re building a financial platform for local businesses across Europe - one built around simple tools, thoughtful design and real human support. Our Members rely on us to help them run their business with confidence, and that responsibility shapes the way we work.

We move fast. We care about quality. We stay close to the detail. And we believe great performance and genuine hospitality should go hand in hand.

If you want to build meaningful products, solve real problems and make a genuine difference for local businesses, we’d love to hear from you

Your Story

• 5+ years' experience in ML Engineering, MLOps, or similar roles.
• Proficiency in Python.
• Experience with a managed machine learning platform such as Amazon SageMaker or an equivalent service.
• Practical knowledge of deploying and operating containerized workloads using Docker and Kubernetes.
• Experience developing or operating model-serving platforms, inference services, or backend APIs.
• Familiarity with feature-store concepts, including feature discovery, reuse, versioning, and 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, including logging, metrics, alerting, and 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.