Responsibilities
- Lead the adoption of machine learning and generative AI systems by analyzing data to uncover bottlenecks and delivering solutions that cut deployment time significantly
- Develop and test technical roadmaps using prototypes, SQL-based analytics, and direct engagement with platforms like Sagemaker, MLflow, Ray, and Bedrock
- Establish measurable outcomes and build dashboards that monitor model behavior and business results
- Create governance structures that support fast experimentation while maintaining compliance, including automated risk and privacy evaluations
- Collaborate with security teams to deploy model monitoring and access safeguards that secure user data while enabling progress
- Design data-driven strategies to lower costs in ML infrastructure as usage expands
- Assess and choose AI technology providers through technical evaluations and return-on-investment analysis
- Work alongside engineering teams to shape scalable system designs, from feature storage to multi-cloud inference setups
- Empower more teams to adopt AI by developing self-serve tools, thorough documentation, and reusable building blocks
Compensation
Competitive salary and benefits package
Work Arrangement
Hybrid work model with flexibility
Team
Part of the core AI infrastructure team driving platform-wide capabilities
Responsibilities
- Lead the adoption of machine learning and generative AI systems by analyzing data to uncover bottlenecks and delivering solutions that cut deployment time significantly
- Develop and test technical roadmaps using prototypes, SQL-based analytics, and direct engagement with platforms like Sagemaker, MLflow, Ray, and Bedrock
- Establish measurable outcomes and build dashboards that monitor model behavior and business results
- Create governance structures that support fast experimentation while maintaining compliance, including automated risk and privacy evaluations
- Collaborate with security teams to deploy model monitoring and access safeguards that secure user data while enabling progress
- Design data-driven strategies to lower costs in ML infrastructure as usage expands
- Assess and choose AI technology providers through technical evaluations and return-on-investment analysis
- Work alongside engineering teams to shape scalable system designs, from feature storage to multi-cloud inference setups
- Empower more teams to adopt AI by developing self-serve tools, thorough documentation, and reusable building blocks
Available for qualified candidates
