Relevant Experience & Education Highlights

Demonstrated hands-on expertise in multi-GPU LLM fine-tuning, kernel development, containerized ML deployments, and multimodal generative AI agents positions this candidate strongly for building scalable foundation model infrastructures at Adobe.

1. Build and optimize infrastructures that power large foundation model training on thousands of GPUs

Engineered high-performance ML systems leveraging multi-GPU setups and custom optimizations.

• Developed multi-GPU LLM fine-tuning pipelines and custom kernel development with benchmarking at a pioneering containerization company.
• Implemented containerized microservice architectures for RL model training and deployment supporting scalable AI workloads.
• Contributed to agentic AI workflows using NeMo Agent Toolkit in NVIDIA AI Enterprise at a leading GPU technology company.

2. Profile GPU utilization, trace inference and training runs and help craft strategies for optimizing our ML model latency

Applied performance profiling and optimization techniques in production ML environments.

• Conducted benchmarking and kernel optimizations for multi-GPU LLM inference and training at a containerization leader.
• Performed agentic red-team testing and remediation workflows to enhance AI model efficiency and reliability.
• Optimized multimodal AI agents combining VLMs and LLMs in a top-tier research lab's NLP group.

3. Architect and optimize end-to-end ML pipelines, ensuring they're scalable, efficient, and robust

Designed and deployed full-stack ML solutions with MLOps best practices.

• Built AI chatbot automating student registration workflows as lead engineer at a non-profit educational organization.
• Engineered recommender systems for personalized content delivery at a stealth startup.
• Integrated Copilot features and shaped product roadmaps for enterprise communication tools at a major software company.

Requirements & Candidate Alignment