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Lead implementation of production-grade ML systems: data pipelines, training, evaluation, and scalable GPU inference. Fine-tune/adapt large models (LoRA, QLoRA, SFT, DPO, distillation), optimize latency/cost/reliability, build evaluation and safety pipelines, and integrate ML into backend and client applications while operating under production constraints.

Lead the execution of production ML systems: build data pipelines, training workflows, evaluation and inference infrastructure, fine-tune large models (LoRA/QLoRA/SFT/DPO), optimize GPU deployments, and integrate ML into products while ensuring robustness, safety, and reliable user-facing performance.

Lead the end-to-end ML execution: build and operate data pipelines, training and evaluation systems, and scalable inference. Fine-tune and optimize large models (LoRA/QLoRA/SFT/DPO/distillation), ensure GPU efficiency, monitor production quality, collaborate with product and application engineering, and rapidly ship reliable, safe ML solutions that translate research into production.

Design, build, and operate large-scale ML systems across data, training, evaluation, inference, and deployment. Lead GPU-optimized training and inference pipelines, ensure reliability, performance, safety, and seamless integration into products. Own production deployments, monitoring, and cross-functional best practices to drive measurable model improvements and efficient research-to-production cycles.

Lead the end-to-end execution layer for production machine learning systems, spanning data, training, fine-tuning, evaluation, inference, deployment, monitoring, and continuous improvement. Architect scalable GPU-based systems, optimize performance and reliability, build high-quality data pipelines, and translate research advances into product improvements. Provide hands-on technical leadership, make pragmatic trade-offs, and support rapid, measurable, safe iteration.

Lead development of production-grade ML systems: own end-to-end pipelines, fine-tuning (LoRA/QLoRA/SFT/DPO), scalable inference, GPU optimization, evaluation, and deployment. Collaborate with application engineers to integrate models, prioritize latency, cost, reliability, safety, and ship iterative improvements.

Lead implementation of production-grade ML systems: data pipelines, training/evaluation workflows, scalable GPU inference, model fine-tuning (LoRA/QLoRA/SFT/DPO), deployment, and monitoring. Collaborate with product and application teams to ship reliable, efficient, and safe ML-powered features for real users while managing latency, cost, and robustness constraints.

Lead execution of end-to-end ML systems: data pipelines, training, evaluation, and scalable inference. Fine-tune and deploy large models (LoRA/QLoRA/SFT/DPO/distillation), optimize GPU performance, ensure robustness, safety, and reliability, and integrate ML into product backends and clients while supporting team delivery and production stability.

Lead end-to-end ML execution: build and operate data pipelines, training workflows, evaluation, and scalable inference. Fine-tune large models (LoRA/QLoRA/SFT/DPO), optimize GPU deployments, implement robustness and safety evaluations, and collaborate with product and engineering to ship reliable production ML systems.

Own end-to-end machine learning systems spanning data, training, fine-tuning, evaluation, inference, and deployment. Build scalable pipelines for large models, optimize GPU-based inference for latency, cost, memory, and reliability, and establish production monitoring and continuous improvement. Partner with research and application engineering to translate model capabilities into reliable products. This hands-on technical leadership role requires strong software and systems fundamentals, independent judgment, and a focus on measurable experimentation and production quality.