teya - Senior Machine Learning Engineer (ML Platform)

• 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. • 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. • 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.