Develop and manage scalable automated machine learning pipelines, CI/CD workflows, and high-throughput model serving infrastructure. Design and maintain feature stores and data pipelines to ensure efficient model training and reliability.
Lead and mentor a team of MLOps engineers to define the infrastructure roadmap and establish operational best practices. Architect scalable ML pipelines, model serving infrastructure, and monitoring strategies to bridge the gap between research and production.
Research and develop data science methods to improve the predictive performance of the NEXUS Large Tabular Model across diverse enterprise datasets. Collaborate with R&D and Engineering teams to ship production-grade Python components and validate approaches on real customer data.
Own the end-to-end reliability, performance, and scalability of Extensions capabilities within the NEXUS backend. Design distributed workflows and collaborate with data scientists to translate complex requirements into production-grade engineering implementations.
Facilitate the adoption of the NEXUS Large Tabular Model by deploying production use cases and proving ROI against legacy baselines. Act as a technical bridge between customers and internal product teams to translate field insights into the product roadmap.
Develop and optimize a large neural network-based tabular model, focusing on performance bottlenecks and memory efficiency. Rewrite critical Python components in Rust or C++ to improve latency and throughput across ML pipelines.
You will design, build, and maintain production model serving infrastructure for the NEXUS Large Tabular Model using Triton Inference Server. This role involves optimizing inference pipelines for latency and throughput while managing resource observability and performance tuning.
