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The Principal MLOPs Engineer will architect and optimize an ML inference platform, ensuring scalability in a production environment. Responsibilities include collaborating with teams to develop robust systems, providing mentorship, and applying model optimization techniques to deployment challenges. This role demands expertise in ML engineering and infrastructure, with a strong focus on Java and familiarity with deep learning frameworks.

The Senior MLOPs Engineer will architect and optimize ML inference platforms, ensuring robustness and cost-effectiveness. Responsibilities include collaborating with teams, technical leadership, and ongoing system operations, with a focus on cutting-edge machine learning and deep learning models.

Develop scalable Java code for content aggregation, indexing, retrieval, and ranking in a large-scale distributed system running in a k8s environment with zero downtime. Collaborate with team members, write design documents, develop automated tests, and coordinate deployments. Requires in-depth knowledge of algorithms, data structures, and E2E software development lifecycle experience.