• Lead the design, development, and ownership of AI-powered internal platforms and systems, including initiatives built from the ground up. • Own end-to-end delivery of complex projects, from problem discovery and system design through production rollout and iteration. • Design and implement AI-driven features, intelligent automation, and orchestration systems that enhance customer experiences and compound productivity across teams. • Embed AI capabilities into customer-facing products, internal tools, and operational workflows, ensuring solutions integrate naturally and deliver measurable value. • Build full-stack systems spanning backend services, APIs, data layers, and frontend interfaces. • Translate ambiguous business and operational problems into scalable, maintainable, AI-enabled technical solutions. • Apply strong systems thinking to ensure reliability, performance, security, permissions, and auditability across platforms. • Collaborate closely with engineers, teams, and stakeholders across the company to identify high-impact opportunities and drive adoption. • Participate in production support and incident response for owned systems, helping define and uphold reliability and operational best practices. • Mentor AI team members through code reviews, design feedback, and hands-on guidance, raising the overall quality bar of the team.

• Lead the design and development of scalable machine learning infrastructure on AWS • Work closely with product teams to develop MVPs for AI-driven features • Create and enhance monitoring and alerting systems for machine learning models • Enable various departments to leverage AI/ML models for different use cases. • Offer expertise in debugging and resolving issues related to ML models in production • Design and scale ML architecture to support rapid user growth • Conduct code reviews, mentor team members and elevate overall team capabilities through knowledge sharing

• Design and build production-grade AI systems • Make principled architecture choices regarding RAG and fine-tuning • Build comprehensive evaluation and observability frameworks • Create production-quality Python services • Manage the economics of LLM usage • Partner with Product and Data Science teams • Mentor junior engineers on AI craft • Establish standards for performance and data governance

• Architect, design, and implement scalable AI platform services that support the deployment, orchestration, and lifecycle management of LLMs and other AI models. • Apply LLMs and other AI technologies directly to build and enhance ClickUp’s intelligent features, working closely with product and engineering teams to deliver impactful solutions. • Build and maintain robust APIs and backend systems that enable seamless integration of AI-powered features into ClickUp’s core platform. • Develop infrastructure for model serving, monitoring, logging, and automated evaluation to ensure high reliability and performance of AI services in production. • Integrate with multiple LLM providers (e.g., OpenAI, Anthropic, Google) and manage model selection, routing, and fallback strategies for optimal performance and cost. • Drive the adoption of best practices in AI privacy, security, and compliance, including data anonymization, secure data handling, and regulatory adherence. • Optimize platform performance, scalability, and cost-efficiency, leveraging cloud-native technologies and distributed systems. • Stay current with advancements in AI infrastructure, MLOps, and LLM applications, and proactively incorporate relevant innovations into ClickUp’s AI platform. • Collaborate cross-functionally with product, frontend, and data teams to deliver seamless, reliable, and user-centric AI experiences.