em> Represent ML platform strategy at the executive and cross-company level Balance long-term platform investment with near-term product acceleration and measurable business impact Technical Product Leadership Lead product strategy for end-to-end ML lifecycle capabilities, including training, deployment, monitoring, iteration, and model governance Drive roadmap and prioritization for real-time and batch inference infrastructure Lead product direction for feature stores, ML data platforms, data pipelines, and offline/online consistency frameworks Enable experimentation platforms, model evaluation workflows, and safe rollout practices for AI and ML systems We aim to improve developer productivity. Experience with MLOps platforms, orchestration, CI/CD for ML, model observability, drift detection, and safe deployment practices Fluency with data analysis workflows, including SQL, Python, telemetry, experimentation data, and platform health metrics Executive communication skills, with the ability to translate technical platform work into business impact, strategic tradeoffs, and clear decision-making Proven ability to align engineering, data science, infrastructure, product, and business stakeholders across matrixed organizations Preferred Qualifications: Experience in building or improving machine learning platforms.