Leads multiple technology projects and engineering teams while designing and implementing large-scale, distributed, cloud-native systems. Provides technical leadership across teams, shapes architecture and platform roadmaps, mentors engineers, drives reliability and performance, and partners with product leaders. Uses modern programming languages, cloud platforms, microservices, databases, containers, CI/CD, observability, and AI-based development tools to deliver secure solutions supporting regulatory and customer needs.

Conduct applied AI research and develop foundation models from design through production deployment. Collaborate with data scientists, software and machine learning engineers, and product managers to build scalable AI products. Research deep learning, language, graph, sequential, and behavioral models; optimize training and inference; reproduce emerging research; deliver platform or solution code; and publish internal or external technical findings.

Design, develop, deploy, and support production AI systems, including foundation model training, LLM inference, agentic workflows, vector search, guardrails, evaluation, governance, and observability. Optimize model performance, scalability, latency, throughput, hardware utilization, and cost. Architect multi-model orchestration pipelines, lead technical governance and design reviews, define AI engineering standards, and mentor senior technical staff.