• Design, build, and maintain production recommendation systems at consumer scale. • Develop ranking and retrieval models to improve personalization and user experience. • Own ML pipelines end-to-end, from feature engineering and training to deployment and monitoring. • Collaborate with client stakeholders and engineering teams to deliver scalable ML solutions. • Analyze large-scale user behavior data and run A/B tests to optimize recommendation performance. • Improve reliability, scalability, and latency of production ML systems on AWS/GCP/Azure.

• Lead and manage a team of Forward-Deployed Machine Learning Engineers (FDMLEs) delivering customer-facing AI and ML solutions from prototyping through production deployment and iteration. • Support team growth through coaching, mentorship, and career development while fostering a strong culture of ownership, customer focus, and operational excellence. • Drive execution in ambiguous, fast-paced environments by enabling rapid experimentation, iterative development, and close cross-functional collaboration. • Partner with Federato leadership across Forward Deployed Engineering, Data Science, and Customer Success to align priorities, delivery expectations, and customer outcomes. • Translate strategic AI initiatives into actionable roadmaps, priorities, and delivery milestones, ensuring projects are effectively scoped, staffed, and executed. • Establish and improve agile workflows tailored to forward-deployed AI and ML work, including sprint planning, technical design reviews, backlog grooming, retrospectives, and operational follow-through. • Serve as the primary coordination point across internal teams and customer stakeholders to ensure alignment, transparency, issue resolution, and successful adoption of deployed solutions. • Drive operational excellence through strong communication, documentation, monitoring, and incident-management practices across customer-facing ML systems.

• Architect and optimize distributed data pipelines to process massive multi-sensor logs. • Develop and tune both heuristic-based and ML-assisted algorithms to automatically classify and describe complex scenarios. • Extract and format scenario data utilizing the Pegasus layer standard to ensure semantic consistency. • Manage the ingestion of tagged events into the observations database, enabling high-speed querying and retrieval for ML training. • Operate with broad autonomy to drive consensus across organizational boundaries, collaborating with downstream consumers. • Guide, mentor, and elevate less-experienced engineers.

• Develop pipelines for post-training tasks such as fine-tuning, evaluation, and model compression. • Implement scalable systems for model deployment, monitoring, and optimization. • Collaborate with researchers to validate experimental results in production contexts. • Build tools to automate benchmarking and regression testing. • Identify opportunities to improve efficiency in resource utilization and inference speed.