Requirements

5+ years' experience in ML Engineering, MLOps, or similar roles
Proficiency in Python
Experience with managed machine learning platforms like Amazon SageMaker or equivalents
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 discovery, reuse, versioning, and online/offline consistency
Knowledge of model registries, experiment tracking, and ML metadata management
Experience with performance, scalability, and reliability of real-time systems
Hands-on experience provisioning and managing cloud infrastructure with Terraform
Experience with CI/CD pipelines, automated testing, and Git-based 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

What You'll Be Doing

Developing and maintaining platforms, services, and tooling for deploying, serving, and operating ML models in production
Deploying and operating machine learning workloads across ML infrastructure
Improving automation across the ML lifecycle, including packaging, deployment, versioning, monitoring, and release processes
Maintaining and enhancing reliability and observability of the ML platform and model-serving services with logging, metrics, and alerting
Participating in on-call rotations, investigating production incidents, and implementing improvements to prevent recurrence
Collaborating with data scientists, software engineers, and platform teams to convert ML use cases into reliable production solutions
Engaging in technical discussions and code reviews to ensure maintainable designs and strong engineering standards
Creating and maintaining clear technical documentation and operational runbooks

Inclusive and equal opportunity work environment
Commitment to accessibility and fair experience during recruitment processes
Supportive culture valuing diversity, strong outcomes, and meaningful work for local businesses