Defines enterprise architecture, engineering standards, governance, and innovation roadmaps for public-cloud data, analytics, AI/ML, and agentic AI platforms. Leads cloud transformation, lakehouse and real-time analytics architecture, AI-agent frameworks, MLOps, governance, security, reliability, automation, and regulatory compliance. Partners with engineering, cybersecurity, risk, product, and business teams to deliver scalable, secure, resilient, and cost-efficient data and AI ecosystems.

Lead end-to-end ML implementations for clients, design and maintain production-grade MLOps pipelines, deploy and optimize Generative AI/LLM and NLP solutions, manage infrastructure (Docker, orchestration, distributed compute), and communicate technical results to stakeholders.

Design and maintain robust CI/CD pipelines, manage integrations with large customer repositories, develop full-stack software, provision cloud infra with Terraform, and implement scalable data pipelines. Learn and apply PySpark, modern data architecture, and AI/agentic tools (Claude, Cursor) to automate and accelerate engineering workflows.