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About the Role:
- Define technical vision for ML platform supporting generative AI at scale.
- Partner with leadership to translate business priorities into strategy.
- Influence engineering practices across multiple teams.
Key Responsibilities:
- Drive MLOps standards, model registries, and secure infrastructure.
- Optimize large-scale model training and inference for cost/performance.
- Guide senior engineers and raise the bar for ML systems.
Requirements:
- Expert-level Python and Java with production-grade systems.
- Deep experience with MLflow, Kubeflow, Ray, and cloud platforms.
- Strong knowledge of Kubernetes, Docker, and distributed systems.
Benefits:
- Competitive base salary of $176,000–$195,000 per year.
- Fully remote option in Canada or based in Mississauga.
Jobgether
The hiring company builds machine learning platforms and MLOps solutions for large-scale AI development. It offers a collaborative and specialized environment with a focus on technical excellence and innovation.