Main tasks & responsibilities
- Design and build our central MLOps Platform covering the complete ML lifecycle: data ingestion, feature engineering, training, deployment, monitoring, and retraining at scale
- Architect robust CI/CD workflows and self-service capabilities that empower data scientists to deploy models independently while maintaining security, compliance, and operational excellence
- Partner with Data Scientists, ML Engineers, and Infrastructure teams to translate requirements into platform capabilities, provide technical guidance, and drive seamless integration
- Establish and evangelize MLOps best practices across the organization, driving adoption through training, documentation, and hands-on support
- Optimize infrastructure costs and enhance observability by implementing efficient resource allocation, comprehensive monitoring, and intelligent automation that eliminates operational toil
Requirements
- 5+ years of experience in MLOps, ML Engineering, or DevOps with proven success building end-to-end MLOps platforms in production environments
- Deep hands-on expertise with AWS SageMaker AI & SageMaker Unified Studio, including pipelines, model registry, endpoints, and feature store
- Solid understanding of the ML lifecycle and experience with containerization (Docker, Kubernetes), CI/CD tools, and ML monitoring/observability
- Demonstrated track record driving organizational adoption of new platforms and processes across multiple teams
- Exceptional communication skills: you can articulate complex technical concepts to both technical and non-technical stakeholders and influence decision-making. You can do this while being fluent in English and working in a culturally diverse, international team
- Solution-oriented mindset with a passion for automation: you're a continuous learner who stays current with ML/AI trends and brings innovation to the team