Partner with product and engineering leadership to translate objectives into a multi-quarter
Define architectures and standards for scalable data and ML pipelines for training, evaluation
Set direction for MLOps, including CI/CD for models, model registry, feature stores, and experiment
Establish reliability, observability, and performance practices for ML systems in production
Define secure integration patterns with existing systems, ensuring authentication, RBAC, audit
Provide technical leadership and mentorship across engineering teams for ML infrastructure.

🎯 Requirements

Expert level in Python and Java with strong software engineering fundamentals.
Extensive experience designing/building ML platforms and ML Ops at scale; familiarity with MLFlow
Extensive cloud experience (AWS, Azure, and/or GCP), containerization (Docker), and orchestration
Proven track record setting technical direction and driving cross-team initiatives.

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