• General Responsible for Algo Collateral Architecture and DevOps & AI CoE trajectory and implementation for LC&I. • Drive feature development & support end-2-end. • Engage with business stakeholders, other squads, and tribes. • Mentor squad members. • Drive code quality improvements. • Improve DevOps, monitoring. • Drive down IT-run.

• Write well designed, testable, efficient code by using best software development practices. • Contribute in all phases of the development lifecycle. • Support continuous improvement by investigating alternatives and technologies and presenting these for architectural review. • Proposes initial technical implementation which supports architectural changes that solve scaling and performance problems. • Work with version control using GitHub, conflict resolution, and automation of continuous integration processes. • Create and maintain software documentation.

• Lead the design, development, and delivery of advanced Natural Language Understanding (NLU) solutions leveraging Large Language Models (LLMs) for text and reasoning tasks. • Own the technical integrity and quality of NLU solutions, ensuring robust, scalable, and maintainable code and models. • Oversee architectural decisions, code reviews, and best practices for LLM-based NLU systems. • Ensure timely and successful delivery of NLU features and improvements, proactively identifying and mitigating risks. • Operate with high independence, making critical technical decisions and setting standards for the team. • Lead initiatives involving complex reasoning, multi-turn dialogue, and advanced text understanding, often spanning multiple projects or products. • Drive measurable improvements in NLU accuracy, performance, and customer satisfaction. • Mentor engineers and data scientists, fostering a culture of technical excellence and continuous learning.

• Own and deliver fullstack product features • Design, build, and ship features end-to-end across frontend, backend, and infrastructure • Take ideas from concept → production with minimal hand-holding • Ensure what you ship works in the real product, not just in isolation • Think in systems, not just components • Translate product goals into technical solutions • Make pragmatic trade-offs between speed, quality, and scalability • Design solutions that are maintainable and evolve with the product • Build production-quality systems • Write clean, reliable, and scalable code across the stack • Improve performance, observability, and reliability of existing systems • Debug and fix issues in live environments • Collaborate closely with product, data, and design • Challenge requirements and improve solutions • Think about user impact, not just implementation • Use AI as a force multiplier • Use tools like Cursor, Copilot, and LLMs to accelerate delivery • Build and integrate AI-powered features where appropriate • Maintain engineering quality while moving fast • Contribute to engineering standards • Raise the quality bar across the codebase • Mentor other engineers where needed • Improve developer experience, tooling, and workflows