Key skills
AIMLClaudeMLflowKubeflowData EngineeringAnalyticsSnowflakeSageMakerLeadershipMentoringCommunication
About this role
Role Overview
- Architect and implement scalable data platform tools that support AI product development, enabling teams to deploy and operate AI models effectively
- Act as the technical bridge between AI Product teams and Platform Engineering
- Lead the design of cross-functional data architecture, ensuring consistency, scalability, and reliability across reporting, analytics, and ML use cases
- Partner with engineering leadership to establish platform standards, governance frameworks, and best practices
- Manage and maintain the data platform (Snowflake, orchestration) with a focus on reliability, performance, and cost
- Ensure platform documentation is up-to-date and establish robust data quality KPIs and monitoring within your first 9 months
Requirements
- Proven Data Engineering background with clear progression toward architecture responsibilities, including deep expertise in designing and scaling data platforms
- Strong understanding of the full ML lifecycle: experimentation, training, deployment, monitoring, and retraining, with experience using modern ML platforms and tools (MLflow, Kubeflow, SageMaker, TensorBoard, or similar)
- Proven ability to bridge technical and product perspectives, translating business needs into technical solutions, with a track record of making complex trade-offs and driving technical consensus
- Track record of influencing technical direction and mentoring engineering teams, with excellent communication skills to articulate technical vision to diverse stakeholders
- Experience working in regulated environments (financial services preferred) is a plus, with a strong mindset for governance and compliance.
Benefits
- unlimited access to the best AI tools on the market
- Claude Code, Cursor, Copilot, Dust, and Notion AI