• Design, build, and maintain robust data pipelines for ingestion, transformation, and feature engineering • Develop, train, evaluate, and iterate on machine learning models across classification, regression, clustering, and NLP tasks • Fine-tune and adapt pre-trained LLMs and foundation models for specific use cases and datasets • Build and manage MLOps infrastructure including model versioning, experiment tracking, and deployment pipelines • Work with structured and unstructured data at scale — including text, tabular, and time-series data • Monitor model performance in production and implement retraining and drift-detection strategies • Collaborate with engineering and product teams to translate data insights into actionable AI features • Document data schemas, model architectures, and pipeline logic clearly and thoroughly

• Lead dynamic live discussions that foster interaction and deepen understanding. • Deliver clear, constructive, and authentic feedback on student submissions, including recorded video responses. • Manage online discussions, respond promptly to student inquiries, and track student progress. • Facilitate a minimum of 1-2 courses per month with consistent engagement and preparation. • Complete an in-depth onboarding program, including shadowing live courses, participating in debrief sessions, and mastering the assigned certificate program. • Engage in ongoing training and professional development to stay current with emerging learning methodologies, educational technologies, and best practices in online facilitation.

• Build and scale experimentation services • Develop RESTful APIs using FastAPI • Collaborate with data scientists and product teams • Optimize data processing using PySpark

• Own the ML function end to end: You hold the people, the priorities, the strategy, and the outcomes. This isn't a coordination role. You're the single accountable leader for how the ML function performs inside Rosso. • Set and sign off on ML strategy: Work with your ML engineers and Experts to develop strategic direction. Propose it, debate it, sign off with the GM. When there's alignment, operate with a high degree of autonomy. • Build a high-performing team: Lead hiring, onboarding, performance management, and career development. Set the frameworks and operating rhythms that give ML engineers clarity, support, and room to grow. Act on underperformance. Hold the hiring bar high as the team scales. • Own the operating systems: Build and maintain the rituals and structures that keep the team effective - sprint cadences, incident review, model monitoring feedback loops, cross-team reporting, and the prioritisation processes that keep the function focused on what matters. • Enable without adding overhead: You are a sounding board, not a technical authority. Ask the right questions, help surface risks, and create space for experts to make good decisions - without positioning yourself as another review layer. • Drive collaboration with the Rosso Engineering Manager: Partner closely to align priorities between ML and software engineering. The two teams need to work together effectively, and you are a key part of making that happen.