Teams at Lutra

Drive product strategy and execution for ML-driven geospatial systems: translate research into production roadmaps, own execution frameworks, manage dependencies across data engineering, ML, and cloud teams, lead sprint rituals, and communicate technical tradeoffs and delivery status to senior leadership and external stakeholders.

Design and lead end-to-end architectures for production machine-learning systems at extraordinary scale. Responsibilities span data preparation, feature generation, model delivery, deployment, hosting, inference, monitoring, performance optimization, and technical design. The role establishes engineering standards, influences ML architecture, and collaborates across data science and software engineering to build reliable, maintainable systems for high-volume, low-latency workloads.

Design and operate globally distributed database systems across bare-metal and cloud environments. Lead reliability, performance, automation, observability, incident response, capacity planning, security, and database change management. Establish technical direction, simplify the infrastructure footprint, develop data-platform roadmaps, troubleshoot complex distributed-system failures, and mentor engineers across teams.