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Owns end-to-end machine learning systems across data, training, evaluation, inference, deployment, and monitoring. Builds large-model training and fine-tuning pipelines, evaluation frameworks, high-performance GPU inference systems, and reliable production infrastructure. Partners with research and application engineering to convert model capabilities into measurable product improvements. Provides hands-on technical leadership, makes pragmatic trade-offs, and ensures ML systems are scalable, efficient, safe, reliable, and production-ready.

Leads development of production machine learning systems across data, training, fine-tuning, evaluation, inference, deployment, monitoring, and optimization. Builds scalable large-model pipelines, high-performance GPU infrastructure, and reliable production systems while partnering with research and application engineering. Owns technical execution, performance measurement, debugging, system reliability, and translating model capabilities into product improvements.

Lead execution of end-to-end ML systems: data pipelines, training workflows, evaluation, and inference. Translate research into production by fine-tuning large models (LoRA/QLoRA/SFT/DPO/distillation), designing scalable GPU inference, ensuring memory/latency optimization, building evaluation for performance/robustness/safety, and integrating ML into backend and client products while operating under real-world constraints.

Lead production-grade ML systems: build data pipelines, training and evaluation workflows, and scalable GPU inference. Fine-tune and adapt large models (LoRA, QLoRA, SFT, DPO, distillation), optimize deployments for latency/cost/reliability, and collaborate with product and engineering to ship robust, observable model-driven features.

Lead production-grade ML systems: build and operate end-to-end ML pipelines, fine-tune large models (LoRA/QLoRA/SFT/DPO/distillation), design scalable inference, ensure GPU optimization, implement evaluation for performance/robustness/safety, and collaborate with product and application teams to ship reliable, low-latency AI features.

Lead the execution of ML systems from data pipelines and training to inference and deployment. Build scalable GPU-based training and inference, apply fine-tuning methods (LoRA/QLoRA/SFT/DPO/distillation), design data and evaluation pipelines for performance, robustness, and safety, and integrate ML systems into product backends and clients while optimizing latency, cost, and reliability.

Own end-to-end ML systems: data, training, evaluation, inference, and deployment for production-grade LLMs. Build scalable training and inference pipelines, optimize GPU performance and memory, maintain evaluation and safety tooling, and collaborate with application engineers to integrate models into products while balancing latency, cost, and reliability.

Own the end-to-end machine learning execution layer, including data pipelines, large-model training and fine-tuning, evaluation, inference, deployment, monitoring, and reliability. Architect scalable GPU-based systems, optimize latency and cost, translate research into production products, and establish measurable, safe model-improvement processes. This hands-on technical leadership role requires strong software engineering, systems expertise, independent judgment, and experience shipping production ML systems.

Leads the end-to-end execution layer for machine learning, including data pipelines, large-model training and fine-tuning, evaluation, inference, deployment, monitoring, and reliability. Designs scalable GPU-based systems, optimizes performance and cost, partners with research and application engineering, and translates model capabilities into measurable production improvements. Also provides technical leadership, judgment, and support for high-impact ML delivery.

Lead end-to-end machine learning systems across data, training, fine-tuning, evaluation, inference, and deployment. Build scalable large-model pipelines, optimize GPU-based inference for latency, cost, memory, and reliability, and establish production monitoring and continuous improvement infrastructure. Partner with research and application engineering to translate model capabilities into reliable product improvements while supporting team delivery and maintaining high standards for correctness and quality.