Leads architecture and technical strategy for complex cloud, data, and AI/ML systems. Designs scalable multi-cloud platforms, data pipelines, MLOps and LLM applications; establishes infrastructure, DevOps, security, governance, and observability standards. Partners with clients and leadership, leads modernization initiatives, resolves difficult integration challenges, mentors senior engineers, and evaluates emerging technologies.

Leads the technical vision and delivery of an enterprise agentic AI program. Oversees architecture, evaluation, deployment, MLOps, reliability, security, and scalability across multiple workstreams. Partners with executives and client stakeholders on roadmaps and high-stakes decisions, aligns data science and engineering teams, establishes technical standards, and mentors senior technical leaders. Requires deep experience in AI/ML, LLMs, agentic systems, AWS, distributed systems, evaluation, and production-scale software engineering.

Train and operate production machine learning models on Amazon SageMaker using AWS Trainium. Optimize PyTorch code for Trainium compilation, memory, throughput, and distributed training; diagnose hardware, compiler, device, data, and code issues; build cost-aware end-to-end training pipelines; and collaborate directly with client and internal engineering teams to deliver production workloads.