Machine Learning Engineer

• Lead & Mentor a high-performing, skilled, Data Engineering and MLOps team including engineers, senior technical program manager and other technical staff. You'll be responsible for hiring, coaching, and mentoring, with a strong emphasis on developing the careers of your engineers.
• Foster Team and Career Development: Formalize and champion career growth across the Data Infrastructure and MLOps teams. Ensure every team member has a clear career path and an active development plan.
• Drive Technical Strategy & Vision: You will lead the collaborative development of the technical vision and roadmap for our Data and AI/ML platform. You'll partner closely with senior leaders across Risk, Data Analytics, Data Science, and Product Management to co-define the strategy, ensuring it is context-aware of Kiavi's size, capacity, and product needs to drive organization-wide adoption. You will also serve as an "operational steward" for the data stack, prioritizing core infrastructure and maintainability. Your ultimate goal is to build a scaled, efficient, and well-governed platform founded on reproducibility, explainability, bias elimination, and sustainability for all AI/ML initiatives.
• Platform Architecture & Scalability: Work closely with architecture specialists on the team to ensure Kiavi has a scalable core data platform that meets business needs. Identify and prioritize hidden tech debt in the stack that lowers engineer and stakeholder productivity. See that data is efficiently captured, processed, stored, and made accessible across the organization. Drive evolution of the multi-warehouse ecosystem (Snowflake and Databricks) to provide a reliable, high-performance foundation for analytics, machine learning, and business decision-making.
• Foster Customer-Centricity: Support customer-facing initiatives and internal data-dependent teams with a customer orientation. Communicate the “Why” and make certain the platform and infrastructure are built and managed to directly meet customer and internal stakeholder requirements for speed, reliability, and data quality.
• Execute with Excellence: Lead your team in delivering complex, large scale projects where availability, accuracy and security are critical. This includes hands-on technical design, code reviews, and scalable architecture guidance. You will also oversee the data infrastructure layer that powers analytics and AI, ensuring seamless data movement, orchestration, and integration with core engineering systems.

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