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Leads architecture and technical strategy for complex cloud, data, and AI/ML systems. Designs scalable multi-cloud infrastructure, data platforms, MLOps pipelines, model-serving systems, and LLM applications. Establishes infrastructure, DevOps, security, governance, and observability standards; leads migrations and modernization; optimizes performance, reliability, and cost. Serves as senior technical advisor to clients and leadership, mentors engineers, documents architecture, and owns high-stakes technical decisions.

Leads architecture and technical strategy for complex cloud, data, and AI/ML systems. Designs scalable multi-cloud platforms, data pipelines, MLOps infrastructure, LLM applications, and production AI systems while establishing DevOps, security, governance, and reliability standards. Partners with clients and leadership, mentors senior engineers, documents architecture, evaluates emerging technologies, and owns high-impact technical decisions. The role also leads cloud migrations and modernization initiatives.

Design, build, deploy, and maintain AWS-based data and AI/ML infrastructure for enterprise agentic AI platforms. Develop data lake pipelines, observability systems, agent-enablement tooling, and production MLOps/LLMOps capabilities. Own features end-to-end, contribute to architecture and design decisions, optimize reliability, scalability, security, and cost, and collaborate with AWS Professional Services, client teams, and junior engineers.

Lead design and delivery of enterprise generative and agentic AI systems on AWS. Architect scalable, secure Bedrock- and SageMaker-based platforms, validate via Python prototypes, enforce AWS Well-Architected and security best practices, mentor delivery teams, and drive business-aligned production deployments and reusable accelerators.

Build and operate production machine learning training systems on Amazon SageMaker using AWS Trainium. The role focuses on optimizing PyTorch code for Trainium hardware, understanding NeuronCore compilation, diagnosing device-level issues, tuning distributed training for cost and throughput, and delivering production pipelines. The engineer will collaborate directly with client and internal teams in a forward-deployed engineering environment.

Lead design and delivery of enterprise generative and agentic AI systems on AWS. Architect Bedrock-based RAG and multi-agent platforms, prototype in Python, ensure security, observability, IaC, and guide clients from discovery to production.