• Build and own end-to-end ML pipelines spanning data, training, evaluation, inference, and deployment. • Fine-tune and adapt models using state-of-the-art methods such as LoRA, QLoRA, SFT, DPO, and distillation. • Architect and operate scalable inference systems, balancing latency, cost, and reliability. • Design and maintain data systems for high-quality synthetic and real-world training data. • Implement evaluation pipelines covering performance, robustness, safety, and bias, in partnership with research leadership. • Own production deployment, including GPU optimization, memory efficiency, latency reduction, and scaling policies. • Collaborate closely with application engineering to integrate ML systems cleanly into backend, mobile, and desktop products. • Make pragmatic trade-offs and ship improvements quickly, learning from real usage. • Work under real production constraints: latency, cost, reliability, and safety.